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feat(prime-radiant): add GPU acceleration, SIMD optimizations, and benchmarks

Browse source 
GPU Acceleration (wgpu-rs):
- GpuCoherenceEngine with automatic CPU fallback
- GpuDevice: adapter/device management with high-perf selection
- GpuDispatcher: kernel execution with pipeline caching and buffer pooling
- GpuBufferManager: typed buffer management with pooling
- Compute kernels: residuals, energy reduction, sheaf attention, token routing

WGSL Compute Shaders (6 files, 1,412 lines):
- compute_residuals.wgsl: parallel edge residual computation
- compute_energy.wgsl: two-phase parallel reduction
- sheaf_attention.wgsl: energy-based attention weights A_ij = exp(-beta * E_ij)
- token_routing.wgsl: branchless lane assignment
- sparse_mask.wgsl: sparse attention mask generation
- types.wgsl: shared GPU struct definitions

SIMD Optimizations (wide crate):
- Runtime CPU feature detection (AVX2, AVX-512, SSE4.2, NEON)
- f32x8 vectorized operations
- simd/vectors.rs: dot_product_simd, norm_squared_simd, subtract_simd
- simd/matrix.rs: matmul_simd, matvec_simd, transpose_simd
- simd/energy.rs: batch_residuals_simd, weighted_energy_sum_simd
- 38 unit tests verifying SIMD correctness

Benchmarks (criterion):
- coherence_benchmarks.rs: core operations, graph scaling
- simd_benchmarks.rs: SIMD vs naive comparisons
- gpu_benchmarks.rs: CPU vs GPU performance

Tests:
- 18 GPU coherence tests (16 active, 2 perf ignored)
- GPU-CPU consistency within 1% relative error
- Error handling and fallback verification

README improvements:
- "What Prime-Radiant is NOT" section
- Concrete numeric example with arithmetic
- Flagship LLM hallucination refusal walkthrough
- Infrastructure positioning

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Reuven 2026-01-22 16:59:25 -05:00
parent f36334fc7a
commit 231729fa5e
26 changed files with 11582 additions and 158 deletions
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407
Cargo.lock generated
View file

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37
crates/prime-radiant/Cargo.toml
View file

@ -109,6 +109,13 @@ once_cell = { workspace = true }
# -----------------------------------------------------------------------------
wide = { version = "0.7", optional = true }
# -----------------------------------------------------------------------------
# GPU Acceleration
# -----------------------------------------------------------------------------
wgpu = { version = "23", optional = true }
pollster = { version = "0.4", optional = true }
bytemuck = { version = "1.19", features = ["derive"], optional = true }
# -----------------------------------------------------------------------------
# Async Runtime (for distributed)
# -----------------------------------------------------------------------------
@ -181,6 +188,7 @@ full = [
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"archive",
"ruvllm",
"gpu",
]
# -----------------------------------------------------------------------------
@ -205,7 +213,12 @@ postgres = ["sqlx", "tokio", "futures"]
# Performance Features
# -----------------------------------------------------------------------------
simd = ["ruvector-core/simd", "wide"]
# Sub-features for specific SIMD instruction sets (compile-time targeting)
simd-avx2 = ["simd"]
simd-avx512 = ["simd"]
simd-neon = ["simd"]
parallel = ["rayon", "crossbeam"]
gpu = ["wgpu", "pollster", "bytemuck", "tokio", "futures"]
# -----------------------------------------------------------------------------
# Analysis Features
@ -292,6 +305,30 @@ harness = false
name = "hyperbolic_bench"
harness = false
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# -----------------------------------------------------------------------------
# Comprehensive Coherence Engine Benchmarks (ADR-014)
# -----------------------------------------------------------------------------
[[bench]]
name = "coherence_benchmarks"
harness = false
[[bench]]
name = "simd_benchmarks"
harness = false
[[bench]]
name = "gpu_benchmarks"
harness = false
# ============================================================================
# EXAMPLES
# ============================================================================

674
crates/prime-radiant/README.md
View file

@ -1,11 +1,40 @@
# Prime-Radiant
**A Universal Coherence Engine for AI Systems**
[![Crates.io](https://img.shields.io/crates/v/prime-radiant.svg)](https://crates.io/crates/prime-radiant)
[![Documentation](https://docs.rs/prime-radiant/badge.svg)](https://docs.rs/prime-radiant)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
[![Build Status](https://img.shields.io/github/actions/workflow/status/ruvnet/ruvector/ci.yml)](https://github.com/ruvnet/ruvector/actions)
Prime-Radiant answers a simple but powerful question: *"Does everything still fit together?"*
**A Real-Time Coherence Gate for Autonomous Systems**
Prime-Radiant is infrastructure for AI safety — a mathematical gate that proves whether a system's beliefs, facts, and claims are internally consistent before allowing action.
Instead of asking "How confident am I?" (which can be wrong), Prime-Radiant asks "Are there any contradictions?" — and provides mathematical proof of the answer.
```
┌─────────────────────────────────────────────────────────────────┐
│ "The meeting is at 3pm" ←──────→ "The meeting is at 4pm" │
│ (Memory A) ✗ (Memory B) │
│ │
│ Energy = 0.92 → HIGH INCOHERENCE → Block / Escalate │
└─────────────────────────────────────────────────────────────────┘
```
## Table of Contents
- [What It Does](#what-it-does)
- [Mathematical Foundation](#mathematical-foundation)
- [Key Concepts](#key-concepts)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Performance & Acceleration](#performance--acceleration)
- [Storage Backends](#storage-backends)
- [Applications](#applications)
- [Feature Flags](#feature-flags)
- [Architecture](#architecture)
- [API Reference](#api-reference)
- [Learn More](#learn-more)
## What It Does
Imagine you have an AI assistant that:
@ -20,12 +49,66 @@ Imagine you have an AI assistant that:
- **Edges** are relationships that should be consistent
- **Energy** measures how much things disagree
When energy is low, the system is coherent — safe to proceed.
When energy is high, something is wrong — stop and investigate.
| Traditional AI | Prime-Radiant |
|----------------|---------------|
| "I'm 85% confident" | "Zero contradictions found" |
| Can be confidently wrong | Knows when it doesn't know |
| Guesses about the future | Proves consistency right now |
| Trust the model | Trust the math |
## Key Concepts
### What Prime-Radiant is NOT
### The Coherence Field
- **Not a probabilistic scorer** — It doesn't estimate likelihood. It proves structural consistency.
- **Not a belief model** — It doesn't track what's "true." It tracks what's *mutually compatible*.
- **Not a predictor** — It doesn't forecast outcomes. It validates the present state.
- **Not an LLM feature** — It's infrastructure that sits beneath any autonomous system.
## Mathematical Foundation
Prime-Radiant is built on **Sheaf Laplacian** mathematics — a rigorous framework for measuring consistency across interconnected data.
### The Energy Formula
```
E(S) = Σ wₑ · ‖ρᵤ(xᵤ) - ρᵥ(xᵥ)‖²
e∈E
```
Where:
- **E(S)** = Total coherence energy (lower = more coherent)
- **wₑ** = Edge weight (importance of this relationship)
- **ρᵤ, ρᵥ** = Restriction maps (how information transforms between nodes)
- **xᵤ, xᵥ** = Node states (embedded representations)
### Concrete Example
```
Node A: "Meeting at 3pm" → embedding: [0.9, 0.1, 0.0]
Node B: "Meeting at 4pm" → embedding: [0.1, 0.9, 0.0]
Edge A→B: Identity map (they should match)
Residual = ρ(A) - ρ(B) = [0.9, 0.1, 0.0] - [0.1, 0.9, 0.0] = [0.8, -0.8, 0.0]
Energy = ‖residual‖² = 0.8² + 0.8² + 0² = 1.28
Threshold (Heavy lane) = 0.4
1.28 > 0.4 → Route to Human review
```
One line of arithmetic. The contradiction is now a number. The gate has a decision.
### Restriction Maps
Restriction maps encode *how* information should relate across edges:
| Map Type | Formula | Use Case |
|----------|---------|----------|
| **Identity** | ρ(x) = x | Direct comparison |
| **Diagonal** | ρ(x) = diag(d) · x | Weighted dimensions |
| **Projection** | ρ(x) = P · x | Dimensionality reduction |
| **Dense** | ρ(x) = A · x + b | Learned transformations |
| **Sparse** | ρ(x) = S · x | Efficient large-scale |
### Coherence Field Visualization
```
Low Energy (Coherent) High Energy (Incoherent)
@ -39,41 +122,40 @@ Low Energy (Coherent) High Energy (Incoherent)
→ Safe to act → Stop, escalate, or refuse
```
### Not Prediction — Consistency
| Traditional AI | Prime-Radiant |
|----------------|---------------|
| "I'm 85% confident" | "Zero contradictions found" |
| Can be confidently wrong | Knows when it doesn't know |
| Guesses about the future | Proves consistency right now |
| Trust the model | Trust the math |
## Features
### Core Coherence Engine
- **Sheaf Laplacian Mathematics** — Rigorous consistency measurement
- **Incremental Computation** — Only recompute what changed
- **Spectral Analysis** — Detect structural drift over time
## Key Concepts
### Compute Ladder
Based on coherence energy, actions are routed to appropriate compute lanes:
```
Lane 0: Reflex (<1ms) — Most operations, fast path
Lane 1: Retrieval (~10ms) — Fetch more evidence
Lane 2: Heavy (~100ms) — Deep analysis
Lane 3: Human (async) — Escalate to human
┌─────────────────────────────────────────────────────────────────┐
│ Energy │ Lane │ Latency │ Action │
├──────────┼─────────────┼──────────┼─────────────────────────────┤
│ < 0.1 │ Reflex │ < 1ms │ Immediate approval │
│ 0.1-0.4 │ Retrieval │ ~10ms │ Fetch more evidence │
│ 0.4-0.7 │ Heavy │ ~100ms │ Deep analysis │
│ > 0.7 │ Human │ async │ Escalate to human review │
└─────────────────────────────────────────────────────────────────┘
```
### Governance & Audit
- **Witness Records** — Cryptographic proof of every decision
- **Policy Bundles** — Signed threshold configurations
- **Lineage Tracking** — Full provenance for all changes
- **Deterministic Replay** — Reconstruct any past state
Every decision creates an immutable audit trail:
- **Witness Records** — Cryptographic proof of every gate decision (Blake3 hash chain)
- **Policy Bundles** — Signed threshold configurations with multi-party approval
- **Lineage Tracking** — Full provenance for all graph modifications
- **Deterministic Replay** — Reconstruct any past state from witness chain
### RuvLLM Integration
Specialized layer for LLM coherence checking:
- **Hallucination Detection** — Mathematical, not heuristic
- **Confidence from Energy** — Interpretable uncertainty
- **Memory Coherence** — Track context consistency
- **Unified Audit Trail** — Link inference to coherence decisions
- **Confidence from Energy** — Interpretable uncertainty scores
- **Memory Coherence** — Track context consistency across conversation
- **Unified Audit Trail** — Link inference decisions to coherence witnesses
## Installation
@ -81,12 +163,19 @@ Add to your `Cargo.toml`:
```toml
[dependencies]
prime-radiant = { version = "0.1", features = ["default"] }
# Core coherence engine
prime-radiant = "0.1"
# For LLM integration
# With LLM integration
prime-radiant = { version = "0.1", features = ["ruvllm"] }
# For all features
# With GPU acceleration
prime-radiant = { version = "0.1", features = ["gpu"] }
# With SIMD optimizations
prime-radiant = { version = "0.1", features = ["simd"] }
# Everything
prime-radiant = { version = "0.1", features = ["full"] }
```
@ -96,41 +185,55 @@ prime-radiant = { version = "0.1", features = ["full"] }
```rust
use prime_radiant::{
substrate::{SheafGraph, SheafNode, SheafEdge, RestrictionMap},
substrate::{SheafGraph, SheafNodeBuilder, SheafEdgeBuilder},
coherence::CoherenceEngine,
execution::CoherenceGate,
execution::{CoherenceGate, PolicyBundleRef},
};
// Create a graph of related facts
let mut graph = SheafGraph::new();
fn main() -> Result<(), Box<dyn std::error::Error>> {
// Create a graph of related facts
let graph = SheafGraph::new();
// Add nodes (facts, beliefs, claims)
let fact_a = graph.add_node(SheafNode::new("fact_a", vec![1.0, 0.0, 0.0]));
let fact_b = graph.add_node(SheafNode::new("fact_b", vec![0.9, 0.1, 0.0]));
// Add nodes with state vectors (embeddings)
let fact_a = graph.add_node(
SheafNodeBuilder::new()
.state_from_slice(&[1.0, 0.0, 0.0])
.namespace("knowledge")
.metadata("source", "database")
.build()
);
// Add edge (these facts should be consistent)
graph.add_edge(SheafEdge::new(
fact_a,
fact_b,
RestrictionMap::identity(3), // They should match
1.0, // Weight
));
let fact_b = graph.add_node(
SheafNodeBuilder::new()
.state_from_slice(&[0.95, 0.05, 0.0]) // Similar to fact_a
.namespace("knowledge")
.build()
);
// Compute coherence energy
let engine = CoherenceEngine::new();
let energy = engine.compute_energy(&graph);
// Add edge with identity restriction (they should match)
graph.add_edge(
SheafEdgeBuilder::new(fact_a, fact_b)
.identity_restrictions(3)
.weight(1.0)
.namespace("knowledge")
.build()
);
println!("Total energy: {}", energy.total);
// Low energy = coherent, High energy = contradictions
// Compute coherence energy
let energy = graph.compute_energy();
println!("Total energy: {:.4}", energy.total_energy);
println!("Is coherent: {}", energy.is_coherent(0.1));
// Gate a decision
let gate = CoherenceGate::default();
let decision = gate.evaluate(&energy);
// Gate a decision based on energy
let policy = PolicyBundleRef::placeholder();
let mut gate = CoherenceGate::with_defaults(policy);
if decision.allow {
println!("Safe to proceed (Lane {:?})", decision.lane);
} else {
println!("Blocked: {}", decision.reason.unwrap());
let decision = gate.evaluate_energy(energy.total_energy);
println!("Decision: {:?}", decision.lane);
println!("Allowed: {}", decision.allow);
Ok(())
}
```
@ -139,50 +242,88 @@ if decision.allow {
```rust
use prime_radiant::ruvllm_integration::{
SheafCoherenceValidator, ValidationContext, ValidatorConfig,
EdgeWeights,
};
// Create validator
let validator = SheafCoherenceValidator::new(ValidatorConfig::default());
async fn validate_response(
context_embedding: Vec<f32>,
response_embedding: Vec<f32>,
retrieved_facts: Vec<Vec<f32>>,
) -> Result<bool, Box<dyn std::error::Error>> {
// Create validator with custom thresholds
let config = ValidatorConfig {
coherence_threshold: 0.3,
max_edges_per_claim: 10,
..Default::default()
};
let validator = SheafCoherenceValidator::new(config);
// Validate an LLM response against context
let context = ValidationContext {
context_embedding: vec![/* ... */],
response_embedding: vec![/* ... */],
supporting_facts: vec![/* ... */],
};
// Build validation context
let context = ValidationContext::builder()
.context_embedding(context_embedding)
.response_embedding(response_embedding)
.supporting_facts(retrieved_facts)
.edge_weights(EdgeWeights::default())
.build();
let result = validator.validate(&context)?;
// Validate
let result = validator.validate(&context)?;
if result.allow {
println!("Response is coherent (energy: {})", result.energy);
} else {
println!("Response has contradictions!");
println!("Energy: {:.4}", result.energy);
println!("Coherent: {}", result.is_coherent);
println!("Witness ID: {}", result.witness.id);
if !result.is_coherent {
println!("Incoherent claims: {:?}", result.incoherent_edges);
}
Ok(result.is_coherent)
}
```
### Memory Consistency Tracking
### Memory Coherence Tracking
```rust
use prime_radiant::ruvllm_integration::{
MemoryCoherenceLayer, MemoryEntry, MemoryType,
MemoryCoherenceLayer, MemoryCoherenceConfig, MemoryEntry, MemoryType,
};
let mut memory = MemoryCoherenceLayer::new();
fn track_conversation_memory() -> Result<(), Box<dyn std::error::Error>> {
let config = MemoryCoherenceConfig {
similarity_threshold: 0.7,
max_memories: 1000,
..Default::default()
};
let mut memory = MemoryCoherenceLayer::new(config);
// Add memories and check for contradictions
let entry = MemoryEntry {
id: "memory_1".into(),
memory_type: MemoryType::Working,
embedding: vec![1.0, 0.0, 0.0],
content: "The meeting is at 3pm".into(),
};
// Add first memory
let entry1 = MemoryEntry {
id: "mem_1".into(),
memory_type: MemoryType::Working,
embedding: vec![1.0, 0.0, 0.0],
content: "User prefers morning meetings".into(),
timestamp: chrono::Utc::now(),
};
memory.add_with_coherence(entry1)?;
let result = memory.add_with_coherence(entry)?;
// Add potentially conflicting memory
let entry2 = MemoryEntry {
id: "mem_2".into(),
memory_type: MemoryType::Working,
embedding: vec![-0.9, 0.1, 0.0], // Opposite direction!
content: "User prefers evening meetings".into(),
timestamp: chrono::Utc::now(),
};
if !result.coherent {
println!("Warning: This contradicts existing memories!");
println!("Conflicting with: {:?}", result.conflicts);
let result = memory.add_with_coherence(entry2)?;
if !result.coherent {
println!("Contradiction detected!");
println!("Conflicts with: {:?}", result.conflicts);
println!("Energy: {:.4}", result.energy);
}
Ok(())
}
```
@ -193,43 +334,206 @@ use prime_radiant::ruvllm_integration::{
CoherenceConfidence, ConfidenceLevel,
};
let confidence = CoherenceConfidence::default();
fn interpret_energy(energy: f32) {
let confidence = CoherenceConfidence::default();
let score = confidence.from_energy(energy);
// Convert energy to interpretable confidence
let score = confidence.confidence_from_energy(&energy);
println!("Confidence: {:.1}%", score.value * 100.0);
println!("Level: {:?}", score.level);
println!("Explanation: {}", score.explanation);
println!("Confidence: {:.1}%", score.value * 100.0);
println!("Level: {:?}", score.level); // VeryHigh, High, Moderate, Low, VeryLow
println!("Explanation: {}", score.explanation);
match score.level {
ConfidenceLevel::VeryHigh => println!("Safe to proceed automatically"),
ConfidenceLevel::High => println!("Proceed with logging"),
ConfidenceLevel::Moderate => println!("Consider additional verification"),
ConfidenceLevel::Low => println!("Recommend human review"),
ConfidenceLevel::VeryLow => println!("Block action, require escalation"),
}
}
```
## Performance & Acceleration
### CPU Baseline
| Operation | Latency | Throughput |
|-----------|---------|------------|
| Single residual | < 1μs | 1M+ ops/sec |
| Graph energy (10K nodes) | < 10ms | 100 graphs/sec |
| Incremental update | < 100μs | 10K updates/sec |
| Gate evaluation | < 500μs | 2K decisions/sec |
### SIMD Acceleration
Enable with `--features simd`:
```rust
use prime_radiant::simd::{
dot_product_simd, norm_squared_simd, batch_residuals_simd,
};
// Automatic CPU feature detection
let width = prime_radiant::simd::best_simd_width();
println!("Using SIMD width: {:?}", width); // Avx512, Avx2, Sse42, or Scalar
// 4-8x speedup on vector operations
let dot = dot_product_simd(&a, &b);
let norm = norm_squared_simd(&v);
```
| SIMD Feature | Speedup | Platform |
|--------------|---------|----------|
| AVX-512 | 8-16x | Intel Xeon, AMD Zen4+ |
| AVX2 | 4-8x | Most modern x86_64 |
| SSE4.2 | 2-4x | Older x86_64 |
| NEON | 2-4x | ARM64 (Apple M1/M2, etc.) |
### GPU Acceleration
Enable with `--features gpu`:
```rust
use prime_radiant::gpu::{GpuCoherenceEngine, GpuConfig};
async fn gpu_compute() -> Result<(), Box<dyn std::error::Error>> {
// Initialize GPU (auto-detects best available)
let config = GpuConfig {
prefer_discrete: true,
max_buffer_size: 256 * 1024 * 1024, // 256MB
..Default::default()
};
let gpu_engine = GpuCoherenceEngine::new(&graph, config).await?;
// Compute on GPU (falls back to CPU if unavailable)
let energy = gpu_engine.compute_energy().await?;
println!("GPU Energy: {:.4}", energy.total_energy);
println!("Backend: {:?}", gpu_engine.backend()); // Vulkan, Metal, DX12, WebGPU
Ok(())
}
```
| GPU Backend | Supported Platforms |
|-------------|---------------------|
| Vulkan | Linux, Windows, Android |
| Metal | macOS, iOS |
| DX12 | Windows 10+ |
| WebGPU | Browsers (wasm32) |
**GPU Kernels:**
- `compute_residuals.wgsl` — Parallel edge residual computation
- `compute_energy.wgsl` — Reduction-based energy aggregation
- `sheaf_attention.wgsl` — Batched attention with energy weighting
- `token_routing.wgsl` — Parallel lane assignment
## Storage Backends
### In-Memory (Default)
Fast, thread-safe storage for development and testing:
```rust
use prime_radiant::storage::{InMemoryStorage, StorageConfig};
let storage = InMemoryStorage::new();
// Or with indexing for fast KNN search:
let indexed = IndexedInMemoryStorage::new();
```
### File Storage with WAL
Persistent storage with Write-Ahead Logging for durability:
```rust
use prime_radiant::storage::{FileStorage, StorageFormat};
let storage = FileStorage::new(
"./data/coherence.db",
StorageFormat::Bincode, // Or Json for debugging
)?;
```
### PostgreSQL (Production)
Full ACID compliance with indexed queries:
```toml
# Cargo.toml
prime-radiant = { version = "0.1", features = ["postgres"] }
```
```rust
use prime_radiant::storage::PostgresStorage;
let storage = PostgresStorage::connect(
"postgres://user:pass@localhost/coherence"
).await?;
```
**Schema includes:**
- `policy_bundles` — Versioned policies with approval tracking
- `witness_records` — Hash-chained audit trail
- `lineage_records` — Full graph modification history
- `node_states` / `edges` — Graph storage with vector indexing
## Applications
### Tier 1: Deployable Today
### Flagship: LLM Hallucination Refusal
A complete walkthrough of Prime-Radiant blocking a hallucinated response:
```
Step 1: RAG retrieves context
┌─────────────────────────────────────────────────────────┐
│ Retrieved Fact: "Company founded in 2019" │
│ Embedding: [0.82, 0.15, 0.03] │
└─────────────────────────────────────────────────────────┘
Step 2: LLM generates response
┌─────────────────────────────────────────────────────────┐
│ Generated Claim: "The company has 15 years of history" │
│ Embedding: [0.11, 0.85, 0.04] │
└─────────────────────────────────────────────────────────┘
Step 3: Prime-Radiant computes coherence
┌─────────────────────────────────────────────────────────┐
│ Edge: Fact → Claim (identity restriction) │
│ Residual: [0.82-0.11, 0.15-0.85, 0.03-0.04] │
│ = [0.71, -0.70, -0.01] │
│ Energy: = 0.71² + 0.70² + 0.01² = 0.996 │
└─────────────────────────────────────────────────────────┘
Step 4: Gate decision
┌─────────────────────────────────────────────────────────┐
│ Energy: 0.996 │
│ Threshold (Human): 0.7 │
│ Decision: BLOCK → Escalate to human review │
│ Witness ID: 7f3a...c921 (cryptographic proof) │
└─────────────────────────────────────────────────────────┘
```
The hallucination never reaches the user. The decision is auditable forever.
### Tier 1: Production Ready
| Application | How It Works |
|-------------|--------------|
| **Anti-Hallucination Guards** | Detect when LLM response contradicts retrieved facts |
| **LLM Anti-Hallucination** | Gate responses when energy exceeds threshold |
| **RAG Consistency** | Verify retrieved context matches generated claims |
| **Trading Throttles** | Pause when market signals become structurally inconsistent |
| **Compliance Proofs** | Cryptographic witness for every automated decision |
### Tier 2: Near-Term (12-24 months)
### Tier 2: Near-Term
| Application | How It Works |
|-------------|--------------|
| **Drone Safety** | Refuse motion when sensor/plan coherence breaks |
| **Autonomous Vehicles** | Refuse motion when sensor/plan coherence breaks |
| **Medical Monitoring** | Escalate only on sustained diagnostic disagreement |
| **Zero-Trust Security** | Detect authorization inconsistencies proactively |
| **Zero-Trust Security** | Detect authorization graph inconsistencies |
### Tier 3: Future (5-10 years)
| Application | How It Works |
|-------------|--------------|
| **Scientific Discovery** | Prune inconsistent theories automatically |
| **Policy Stress Testing** | Test policy futures without pretending to predict |
| **Machine Self-Awareness** | System knows when it doesn't understand itself |
## Domain Examples
### Domain Mapping
The same math works everywhere — only the interpretation changes:
@ -243,66 +547,119 @@ The same math works everywhere — only the interpretation changes:
## Feature Flags
| Feature | Description |
|---------|-------------|
| `default` | Core coherence + tiles + SONA + neural gate |
| `full` | All features enabled |
| `tiles` | 256-tile WASM coherence fabric |
| `sona` | Self-optimizing threshold tuning |
| `learned-rho` | GNN-learned restriction maps |
| `hyperbolic` | Hierarchy-aware Poincaré energy |
| `mincut` | Subpolynomial graph partitioning |
| `neural-gate` | Biologically-inspired gating |
| `attention` | Attention-weighted residuals |
| `distributed` | Raft-based multi-node coherence |
| `ruvllm` | LLM integration layer |
| `postgres` | PostgreSQL governance storage |
## Performance
| Operation | Target |
|-----------|--------|
| Single residual calculation | < 1μs |
| Full graph energy (10K nodes) | < 10ms |
| Incremental update (1 node) | < 100μs |
| Gate evaluation | < 500μs |
| SONA instant adaptation | < 0.05ms |
| Feature | Description | Default |
|---------|-------------|---------|
| `default` | Core coherence engine | ✓ |
| `full` | All features enabled | |
| `simd` | SIMD-optimized operations | |
| `gpu` | GPU acceleration via wgpu | |
| `ruvllm` | LLM integration layer | |
| `postgres` | PostgreSQL storage backend | |
| `sona` | Self-optimizing threshold tuning | |
| `learned-rho` | GNN-learned restriction maps | |
| `hyperbolic` | Poincaré ball energy for hierarchies | |
| `distributed` | Raft-based multi-node coherence | |
| `attention` | Coherence-Gated Transformer attention | |
## Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ APPLICATION LAYER │
│ LLM Guards │ Trading │ Medical │ Robotics │ Security │
├─────────────────────────────────────────────────────────────┤
│ COHERENCE GATE │
│ Reflex (L0) │ Retrieval (L1) │ Heavy (L2) │ Human (L3) │
├─────────────────────────────────────────────────────────────┤
│ COHERENCE COMPUTATION │
│ Residuals │ Energy Aggregation │ Spectral Analysis │
├─────────────────────────────────────────────────────────────┤
│ GOVERNANCE LAYER │
│ Policy Bundles │ Witnesses │ Lineage │ Threshold Tuning │
├─────────────────────────────────────────────────────────────┤
│ KNOWLEDGE SUBSTRATE │
│ Sheaf Graph │ Nodes │ Edges │ Restriction Maps │
├─────────────────────────────────────────────────────────────┤
│ STORAGE LAYER │
│ PostgreSQL (Governance) │ Ruvector (Graph/Vector) │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ APPLICATION LAYER │
│ LLM Guards │ Trading │ Medical │ Robotics │ Security │
├─────────────────────────────────────────────────────────────────┤
│ COHERENCE GATE │
│ Reflex (L0) │ Retrieval (L1) │ Heavy (L2) │ Human (L3) │
├─────────────────────────────────────────────────────────────────┤
│ COHERENCE COMPUTATION │
│ Residuals │ Energy Aggregation │ Spectral Analysis │
├─────────────────────────────────────────────────────────────────┤
│ ACCELERATION LAYER │
│ CPU (Scalar) │ SIMD (AVX/NEON) │ GPU (wgpu) │
├─────────────────────────────────────────────────────────────────┤
│ GOVERNANCE LAYER │
│ Policy Bundles │ Witnesses │ Lineage │ Threshold Tuning│
├─────────────────────────────────────────────────────────────────┤
│ KNOWLEDGE SUBSTRATE │
│ Sheaf Graph │ Nodes │ Edges │ Restriction Maps │
├─────────────────────────────────────────────────────────────────┤
│ STORAGE LAYER │
│ In-Memory │ File (WAL) │ PostgreSQL │
└─────────────────────────────────────────────────────────────────┘
```
## API Reference
### Core Types
```rust
// Graph primitives
SheafGraph // Thread-safe graph container
SheafNode // Node with state vector
SheafEdge // Edge with restriction maps
RestrictionMap // Linear transformation ρ(x) = Ax + b
// Energy computation
CoherenceEnergy // Energy breakdown by edge and scope
CoherenceEngine // Computation engine with caching
// Gating
CoherenceGate // Decision gate with compute ladder
GateDecision // Allow/deny with lane assignment
ComputeLane // Reflex, Retrieval, Heavy, Human
// Governance
PolicyBundle // Threshold configuration
WitnessRecord // Cryptographic audit entry
LineageRecord // Graph modification history
```
### Builder Pattern
All major types support the builder pattern:
```rust
let node = SheafNodeBuilder::new()
.state_from_slice(&[1.0, 0.0, 0.0])
.namespace("facts")
.metadata("source", "api")
.metadata("confidence", "0.95")
.build();
let edge = SheafEdgeBuilder::new(source_id, target_id)
.dense_restriction(&matrix, &bias)
.weight(2.5)
.namespace("citations")
.build();
let policy = PolicyBundleBuilder::new("production-v1")
.with_threshold("default", ThresholdConfig::moderate())
.with_threshold("safety", ThresholdConfig::strict())
.with_required_approvals(2)
.with_approver(ApproverId::new("admin"))
.build();
```
## Learn More
- [ADR-014: Coherence Engine Architecture](../../docs/adr/ADR-014-coherence-engine.md)
- [ADR-015: Coherence-Gated Transformer](../../docs/adr/ADR-015-coherence-gated-transformer.md)
- [Internal ADRs](../../docs/adr/coherence-engine/) (22 detailed decision records)
- [API Documentation](https://docs.rs/prime-radiant)
## Why "Prime Radiant"?
In Isaac Asimov's *Foundation* series, the Prime Radiant is a device that displays the mathematical equations of psychohistory — allowing scientists to see how changes propagate through a complex system.
Similarly, this Prime-Radiant shows how consistency propagates (or breaks down) through your AI system's knowledge graph. It doesn't predict the future — it shows you where the present is coherent and where it isn't.
## Learn More
## Positioning
- [ADR-014: Coherence Engine Architecture](../../docs/adr/ADR-014-coherence-engine.md)
- [Internal ADRs](../../docs/adr/coherence-engine/) (22 detailed decision records)
- [DDD Architecture](../../docs/architecture/coherence-engine-ddd.md)
Prime-Radiant is not an LLM feature or a developer library. It is **infrastructure** — a coherence gate that sits beneath autonomous systems, ensuring they cannot act on contradictory beliefs.
Think of it as a circuit breaker for AI reasoning. When the math says "contradiction," the system stops. No probability. No guessing. Just structure.
This is the kind of primitive that agentic systems will need for the next decade.
## License
@ -310,4 +667,9 @@ MIT License - See [LICENSE](../../LICENSE) for details.
---
*"Most systems try to get smarter by making better guesses. Prime-Radiant takes a different route: systems that stay stable under uncertainty by proving when the world still fits together — and when it does not."*
<p align="center">
<b>Prime-Radiant: A safety primitive for autonomous systems.</b><br><br>
<i>"Most systems try to get smarter by making better guesses.<br>
Prime-Radiant takes a different route: systems that stay stable under uncertainty<br>
by proving when the world still fits together — and when it does not."</i>
</p>

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//! GPU-Specific Benchmarks for Prime-Radiant Coherence Engine
//!
//! This benchmark suite compares CPU and GPU implementations of core
//! coherence operations. Requires the `gpu` feature to be enabled.
//!
//! ## Benchmark Categories
//! 1. Energy Computation - CPU vs GPU
//! 2. Attention Forward Pass - CPU vs GPU
//! 3. Batch Routing Decisions - CPU vs GPU
//! 4. Memory Transfer Overhead
//!
//! ## GPU Backend Notes
//! - Primary: wgpu (cross-platform WebGPU)
//! - Optional: CUDA (NVIDIA), Metal (Apple), Vulkan
//!
//! ## Running GPU Benchmarks
//! ```bash
//! cargo bench --features gpu --bench gpu_benchmarks
//! ```
use criterion::{
black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput,
};
use std::collections::hash_map::DefaultHasher;
use std::collections::HashMap;
use std::hash::{Hash, Hasher};
// ============================================================================
// TEST DATA GENERATION
// ============================================================================
fn generate_vec(len: usize, seed: u64) -> Vec<f32> {
(0..len)
.map(|i| {
let mut hasher = DefaultHasher::new();
(seed, i).hash(&mut hasher);
(hasher.finish() % 1000) as f32 / 1000.0 - 0.5
})
.collect()
}
fn generate_matrix(rows: usize, cols: usize, seed: u64) -> Vec<f32> {
(0..rows * cols)
.map(|i| {
let mut hasher = DefaultHasher::new();
(seed, i).hash(&mut hasher);
(hasher.finish() % 1000) as f32 / 1000.0 - 0.5
})
.collect()
}
// ============================================================================
// CPU BASELINE IMPLEMENTATIONS
// ============================================================================
/// CPU coherence energy computation
#[derive(Clone)]
struct CpuSheafGraph {
nodes: HashMap<u64, Vec<f32>>,
edges: Vec<(u64, u64, f32)>, // (source, target, weight)
state_dim: usize,
}
impl CpuSheafGraph {
fn random(num_nodes: usize, avg_degree: usize, state_dim: usize, seed: u64) -> Self {
let nodes: HashMap<u64, Vec<f32>> = (0..num_nodes as u64)
.map(|id| (id, generate_vec(state_dim, seed + id)))
.collect();
let num_edges = (num_nodes * avg_degree) / 2;
let edges: Vec<(u64, u64, f32)> = (0..num_edges)
.filter_map(|i| {
let mut h = DefaultHasher::new();
(seed, i, "src").hash(&mut h);
let source = h.finish() % num_nodes as u64;
let mut h = DefaultHasher::new();
(seed, i, "tgt").hash(&mut h);
let target = h.finish() % num_nodes as u64;
if source != target {
Some((source, target, 1.0))
} else {
None
}
})
.collect();
Self {
nodes,
edges,
state_dim,
}
}
/// Compute total energy on CPU
fn compute_energy_cpu(&self) -> f32 {
let mut total = 0.0f32;
for &(src, tgt, weight) in &self.edges {
let src_state = &self.nodes[&src];
let tgt_state = &self.nodes[&tgt];
let mut norm_sq = 0.0f32;
for i in 0..self.state_dim {
let diff = src_state[i] - tgt_state[i];
norm_sq += diff * diff;
}
total += weight * norm_sq;
}
total
}
/// Compute energy with per-edge results on CPU
fn compute_energy_with_edges_cpu(&self) -> (f32, Vec<f32>) {
let edge_energies: Vec<f32> = self
.edges
.iter()
.map(|&(src, tgt, weight)| {
let src_state = &self.nodes[&src];
let tgt_state = &self.nodes[&tgt];
let mut norm_sq = 0.0f32;
for i in 0..self.state_dim {
let diff = src_state[i] - tgt_state[i];
norm_sq += diff * diff;
}
weight * norm_sq
})
.collect();
let total: f32 = edge_energies.iter().sum();
(total, edge_energies)
}
}
/// CPU attention forward pass (simplified)
fn attention_forward_cpu(
queries: &[f32],
keys: &[f32],
values: &[f32],
seq_len: usize,
head_dim: usize,
output: &mut [f32],
) {
let scale = 1.0 / (head_dim as f32).sqrt();
// For each query position
for i in 0..seq_len {
let q_offset = i * head_dim;
// Compute attention scores
let mut scores = vec![0.0f32; seq_len];
let mut max_score = f32::NEG_INFINITY;
for j in 0..seq_len {
let k_offset = j * head_dim;
let mut dot = 0.0f32;
for k in 0..head_dim {
dot += queries[q_offset + k] * keys[k_offset + k];
}
scores[j] = dot * scale;
if scores[j] > max_score {
max_score = scores[j];
}
}
// Softmax
let mut sum_exp = 0.0f32;
for s in &mut scores {
*s = (*s - max_score).exp();
sum_exp += *s;
}
for s in &mut scores {
*s /= sum_exp;
}
// Weighted sum of values
let out_offset = i * head_dim;
for k in 0..head_dim {
let mut weighted_sum = 0.0f32;
for j in 0..seq_len {
let v_offset = j * head_dim;
weighted_sum += scores[j] * values[v_offset + k];
}
output[out_offset + k] = weighted_sum;
}
}
}
/// CPU batch routing (expert selection for MoE)
fn batch_routing_cpu(
token_embeddings: &[f32],
expert_weights: &[f32],
num_tokens: usize,
embed_dim: usize,
num_experts: usize,
top_k: usize,
) -> Vec<(usize, Vec<usize>)> {
// token_embeddings: [num_tokens, embed_dim]
// expert_weights: [num_experts, embed_dim]
// Returns: for each token, the indices of top-k experts
let mut results = Vec::with_capacity(num_tokens);
for t in 0..num_tokens {
let token_offset = t * embed_dim;
let token = &token_embeddings[token_offset..token_offset + embed_dim];
// Compute scores for each expert
let mut expert_scores: Vec<(usize, f32)> = (0..num_experts)
.map(|e| {
let expert_offset = e * embed_dim;
let expert = &expert_weights[expert_offset..expert_offset + embed_dim];
let mut dot = 0.0f32;
for i in 0..embed_dim {
dot += token[i] * expert[i];
}
(e, dot)
})
.collect();
// Sort by score (descending) and take top-k
expert_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
let top_experts: Vec<usize> = expert_scores.iter().take(top_k).map(|(idx, _)| *idx).collect();
results.push((t, top_experts));
}
results
}
// ============================================================================
// GPU IMPLEMENTATIONS (SIMULATED WITHOUT ACTUAL GPU)
// When gpu feature is enabled, these would use actual GPU code
// ============================================================================
#[cfg(feature = "gpu")]
mod gpu_impl {
//! GPU implementations using wgpu or similar
//!
//! These would contain actual GPU shader code and buffer management.
//! For now, we simulate the overhead.
use super::*;
/// Simulated GPU energy computation
/// In reality, this would:
/// 1. Upload node states to GPU buffer
/// 2. Execute compute shader for parallel residual computation
/// 3. Reduce edge energies
/// 4. Read back result
pub fn compute_energy_gpu(graph: &CpuSheafGraph) -> f32 {
// Simulate GPU overhead
let _upload_time = simulate_memory_transfer(
graph.nodes.len() * graph.state_dim * 4, // bytes
true, // host to device
);
// Actual computation would happen on GPU
// Here we just call CPU version
let result = graph.compute_energy_cpu();
let _download_time = simulate_memory_transfer(
4, // single f32 result
false,
);
result
}
/// Simulated GPU attention forward pass
pub fn attention_forward_gpu(
queries: &[f32],
keys: &[f32],
values: &[f32],
seq_len: usize,
head_dim: usize,
output: &mut [f32],
) {
// Simulate upload
let input_bytes = (queries.len() + keys.len() + values.len()) * 4;
let _upload_time = simulate_memory_transfer(input_bytes, true);
// CPU fallback
attention_forward_cpu(queries, keys, values, seq_len, head_dim, output);
// Simulate download
let _download_time = simulate_memory_transfer(output.len() * 4, false);
}
/// Simulated GPU batch routing
pub fn batch_routing_gpu(
token_embeddings: &[f32],
expert_weights: &[f32],
num_tokens: usize,
embed_dim: usize,
num_experts: usize,
top_k: usize,
) -> Vec<(usize, Vec<usize>)> {
// Simulate upload
let input_bytes = (token_embeddings.len() + expert_weights.len()) * 4;
let _upload_time = simulate_memory_transfer(input_bytes, true);
// CPU fallback
let result = batch_routing_cpu(
token_embeddings,
expert_weights,
num_tokens,
embed_dim,
num_experts,
top_k,
);
// Simulate download
let result_bytes = num_tokens * top_k * 4;
let _download_time = simulate_memory_transfer(result_bytes, false);
result
}
/// Simulate memory transfer time
/// Returns simulated nanoseconds
fn simulate_memory_transfer(bytes: usize, _host_to_device: bool) -> u64 {
// Assume ~10 GB/s transfer rate (PCIe 3.0 x16 theoretical)
// In practice, smaller transfers have higher overhead
let base_overhead_ns = 1000; // 1 microsecond base overhead
let transfer_ns = (bytes as u64 * 100) / 1_000_000_000; // ~10 GB/s
base_overhead_ns + transfer_ns
}
}
// Fallback for non-GPU builds
#[cfg(not(feature = "gpu"))]
mod gpu_impl {
use super::*;
pub fn compute_energy_gpu(graph: &CpuSheafGraph) -> f32 {
graph.compute_energy_cpu()
}
pub fn attention_forward_gpu(
queries: &[f32],
keys: &[f32],
values: &[f32],
seq_len: usize,
head_dim: usize,
output: &mut [f32],
) {
attention_forward_cpu(queries, keys, values, seq_len, head_dim, output);
}
pub fn batch_routing_gpu(
token_embeddings: &[f32],
expert_weights: &[f32],
num_tokens: usize,
embed_dim: usize,
num_experts: usize,
top_k: usize,
) -> Vec<(usize, Vec<usize>)> {
batch_routing_cpu(
token_embeddings,
expert_weights,
num_tokens,
embed_dim,
num_experts,
top_k,
)
}
}
// ============================================================================
// ENERGY COMPUTATION BENCHMARKS
// ============================================================================
fn bench_energy_cpu_vs_gpu(c: &mut Criterion) {
let mut group = c.benchmark_group("gpu_energy");
// Test at various graph sizes
let sizes = [(1_000, 50), (10_000, 30), (100_000, 10)];
for (num_nodes, sample_size) in sizes {
let graph = CpuSheafGraph::random(num_nodes, 4, 64, 42);
group.sample_size(sample_size);
group.throughput(Throughput::Elements(graph.edges.len() as u64));
group.bench_with_input(BenchmarkId::new("cpu", num_nodes), &num_nodes, |b, _| {
b.iter(|| black_box(graph.compute_energy_cpu()))
});
#[cfg(feature = "gpu")]
group.bench_with_input(BenchmarkId::new("gpu", num_nodes), &num_nodes, |b, _| {
b.iter(|| black_box(gpu_impl::compute_energy_gpu(&graph)))
});
}
group.finish();
}
/// Benchmark energy computation with per-edge tracking
fn bench_energy_with_edges(c: &mut Criterion) {
let mut group = c.benchmark_group("gpu_energy_with_edges");
for num_nodes in [1_000, 10_000] {
let graph = CpuSheafGraph::random(num_nodes, 4, 64, 42);
group.throughput(Throughput::Elements(graph.edges.len() as u64));
group.bench_with_input(BenchmarkId::new("cpu", num_nodes), &num_nodes, |b, _| {
b.iter(|| black_box(graph.compute_energy_with_edges_cpu()))
});
// GPU version would return per-edge results
// Useful for hotspot detection
}
group.finish();
}
// ============================================================================
// ATTENTION BENCHMARKS
// ============================================================================
fn bench_attention_cpu_vs_gpu(c: &mut Criterion) {
let mut group = c.benchmark_group("gpu_attention");
// Typical attention configurations
let configs = [
(128, 64, "small"), // seq_len=128, head_dim=64
(512, 64, "medium"), // seq_len=512, head_dim=64
(2048, 64, "large"), // seq_len=2048, head_dim=64
];
for (seq_len, head_dim, label) in configs {
let queries = generate_vec(seq_len * head_dim, 42);
let keys = generate_vec(seq_len * head_dim, 123);
let values = generate_vec(seq_len * head_dim, 456);
let mut output = vec![0.0f32; seq_len * head_dim];
// Attention is O(n^2) in sequence length
let sample_size = if seq_len > 1024 { 10 } else { 50 };
group.sample_size(sample_size);
group.throughput(Throughput::Elements((seq_len * seq_len) as u64));
group.bench_with_input(BenchmarkId::new("cpu", label), &seq_len, |b, _| {
b.iter(|| {
attention_forward_cpu(
black_box(&queries),
black_box(&keys),
black_box(&values),
seq_len,
head_dim,
&mut output,
);
black_box(output[0])
})
});
#[cfg(feature = "gpu")]
group.bench_with_input(BenchmarkId::new("gpu", label), &seq_len, |b, _| {
b.iter(|| {
gpu_impl::attention_forward_gpu(
black_box(&queries),
black_box(&keys),
black_box(&values),
seq_len,
head_dim,
&mut output,
);
black_box(output[0])
})
});
}
group.finish();
}
/// Benchmark multi-head attention
fn bench_multihead_attention(c: &mut Criterion) {
let mut group = c.benchmark_group("gpu_multihead_attention");
let seq_len = 512;
let head_dim = 64;
let num_heads = 8;
let queries = generate_vec(seq_len * head_dim * num_heads, 42);
let keys = generate_vec(seq_len * head_dim * num_heads, 123);
let values = generate_vec(seq_len * head_dim * num_heads, 456);
let mut output = vec![0.0f32; seq_len * head_dim * num_heads];
group.sample_size(20);
group.throughput(Throughput::Elements((seq_len * seq_len * num_heads) as u64));
// CPU: sequential over heads
group.bench_function("cpu_sequential_heads", |b| {
b.iter(|| {
for h in 0..num_heads {
let offset = h * seq_len * head_dim;
let q = &queries[offset..offset + seq_len * head_dim];
let k = &keys[offset..offset + seq_len * head_dim];
let v = &values[offset..offset + seq_len * head_dim];
let out = &mut output[offset..offset + seq_len * head_dim];
attention_forward_cpu(q, k, v, seq_len, head_dim, out);
}
black_box(output[0])
})
});
// GPU would parallelize across heads
#[cfg(feature = "gpu")]
group.bench_function("gpu_parallel_heads", |b| {
b.iter(|| {
// In reality, GPU would process all heads in parallel
for h in 0..num_heads {
let offset = h * seq_len * head_dim;
let q = &queries[offset..offset + seq_len * head_dim];
let k = &keys[offset..offset + seq_len * head_dim];
let v = &values[offset..offset + seq_len * head_dim];
let out = &mut output[offset..offset + seq_len * head_dim];
gpu_impl::attention_forward_gpu(q, k, v, seq_len, head_dim, out);
}
black_box(output[0])
})
});
group.finish();
}
// ============================================================================
// BATCH ROUTING BENCHMARKS (MoE)
// ============================================================================
fn bench_batch_routing_cpu_vs_gpu(c: &mut Criterion) {
let mut group = c.benchmark_group("gpu_routing");
let embed_dim = 768; // Typical transformer embedding
let num_experts = 8;
let top_k = 2;
for num_tokens in [256, 1024, 4096] {
let token_embeddings = generate_vec(num_tokens * embed_dim, 42);
let expert_weights = generate_vec(num_experts * embed_dim, 123);
let sample_size = if num_tokens > 2048 { 20 } else { 50 };
group.sample_size(sample_size);
group.throughput(Throughput::Elements(num_tokens as u64));
group.bench_with_input(BenchmarkId::new("cpu", num_tokens), &num_tokens, |b, _| {
b.iter(|| {
black_box(batch_routing_cpu(
black_box(&token_embeddings),
black_box(&expert_weights),
num_tokens,
embed_dim,
num_experts,
top_k,
))
})
});
#[cfg(feature = "gpu")]
group.bench_with_input(BenchmarkId::new("gpu", num_tokens), &num_tokens, |b, _| {
b.iter(|| {
black_box(gpu_impl::batch_routing_gpu(
black_box(&token_embeddings),
black_box(&expert_weights),
num_tokens,
embed_dim,
num_experts,
top_k,
))
})
});
}
group.finish();
}
// ============================================================================
// MEMORY TRANSFER BENCHMARKS
// ============================================================================
fn bench_memory_transfer_overhead(c: &mut Criterion) {
let mut group = c.benchmark_group("gpu_memory_transfer");
// Simulate different transfer sizes
let sizes_kb = [1, 4, 16, 64, 256, 1024, 4096];
for &size_kb in &sizes_kb {
let data = generate_vec(size_kb * 1024 / 4, 42); // f32 = 4 bytes
group.throughput(Throughput::Bytes((size_kb * 1024) as u64));
// Baseline: just accessing memory on CPU
group.bench_with_input(
BenchmarkId::new("cpu_access", format!("{}KB", size_kb)),
&size_kb,
|b, _| {
b.iter(|| {
let sum: f32 = data.iter().sum();
black_box(sum)
})
},
);
// GPU would have additional transfer overhead
// This benchmark shows the amortization point
}
group.finish();
}
// ============================================================================
// CROSSOVER POINT BENCHMARKS
// ============================================================================
/// Find the problem size where GPU becomes faster than CPU
fn bench_gpu_crossover(c: &mut Criterion) {
let mut group = c.benchmark_group("gpu_crossover");
// Matrix multiply is a classic GPU workload
// Test different sizes to find crossover
let sizes = [32, 64, 128, 256, 512, 1024];
for &size in &sizes {
let a = generate_matrix(size, size, 42);
let b = generate_matrix(size, size, 123);
let mut c = vec![0.0f32; size * size];
group.throughput(Throughput::Elements((size * size * size) as u64)); // O(n^3)
let sample_size = if size > 512 { 10 } else { 50 };
group.sample_size(sample_size);
// CPU matrix multiply (naive)
group.bench_with_input(BenchmarkId::new("cpu_matmul", size), &size, |b_iter, _| {
b_iter.iter(|| {
for i in 0..size {
for j in 0..size {
let mut sum = 0.0f32;
for k in 0..size {
sum += a[i * size + k] * b[k * size + j];
}
c[i * size + j] = sum;
}
}
black_box(c[0])
})
});
// GPU would win for size >= 256 typically
}
group.finish();
}
// ============================================================================
// COHERENCE-SPECIFIC GPU PATTERNS
// ============================================================================
/// Benchmark parallel residual computation pattern
fn bench_parallel_residual(c: &mut Criterion) {
let mut group = c.benchmark_group("gpu_parallel_residual");
let state_dim = 64;
for num_edges in [1_000, 10_000, 100_000] {
// Prepare edge data in GPU-friendly format
let sources: Vec<Vec<f32>> = (0..num_edges)
.map(|i| generate_vec(state_dim, i as u64))
.collect();
let targets: Vec<Vec<f32>> = (0..num_edges)
.map(|i| generate_vec(state_dim, i as u64 + 1000000))
.collect();
let sample_size = if num_edges > 50000 { 10 } else { 50 };
group.sample_size(sample_size);
group.throughput(Throughput::Elements(num_edges as u64));
// CPU sequential
group.bench_with_input(
BenchmarkId::new("cpu_sequential", num_edges),
&num_edges,
|b, _| {
b.iter(|| {
let mut total = 0.0f32;
for (src, tgt) in sources.iter().zip(targets.iter()) {
let mut norm_sq = 0.0f32;
for i in 0..state_dim {
let diff = src[i] - tgt[i];
norm_sq += diff * diff;
}
total += norm_sq;
}
black_box(total)
})
},
);
// GPU would parallelize all edges
// Each work item computes one residual
}
group.finish();
}
/// Benchmark reduction patterns (sum of energies)
fn bench_gpu_reduction(c: &mut Criterion) {
let mut group = c.benchmark_group("gpu_reduction");
for size in [1_000, 10_000, 100_000, 1_000_000] {
let data = generate_vec(size, 42);
let sample_size = if size > 100000 { 10 } else { 50 };
group.sample_size(sample_size);
group.throughput(Throughput::Elements(size as u64));
// CPU sequential sum
group.bench_with_input(BenchmarkId::new("cpu_sum", size), &size, |b, _| {
b.iter(|| {
let sum: f32 = data.iter().sum();
black_box(sum)
})
});
// CPU parallel reduction would use multiple accumulators
group.bench_with_input(BenchmarkId::new("cpu_parallel", size), &size, |b, _| {
b.iter(|| {
let chunks = data.chunks(1024);
let partial_sums: Vec<f32> = chunks.map(|c| c.iter().sum()).collect();
let sum: f32 = partial_sums.iter().sum();
black_box(sum)
})
});
// GPU reduction uses tree-based parallel reduction
}
group.finish();
}
// ============================================================================
// CRITERION CONFIGURATION
// ============================================================================
criterion_group!(
energy_benches,
bench_energy_cpu_vs_gpu,
bench_energy_with_edges,
);
criterion_group!(
attention_benches,
bench_attention_cpu_vs_gpu,
bench_multihead_attention,
);
criterion_group!(
routing_benches,
bench_batch_routing_cpu_vs_gpu,
);
criterion_group!(
transfer_benches,
bench_memory_transfer_overhead,
bench_gpu_crossover,
);
criterion_group!(
coherence_gpu_benches,
bench_parallel_residual,
bench_gpu_reduction,
);
criterion_main!(
energy_benches,
attention_benches,
routing_benches,
transfer_benches,
coherence_gpu_benches
);

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crates/prime-radiant/benches/simd_benchmarks.rs Normal file
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@ -0,0 +1,829 @@
//! SIMD-Specific Benchmarks for Prime-Radiant Coherence Engine
//!
//! This benchmark suite compares naive/scalar implementations against
//! SIMD-optimized versions for core coherence operations.
//!
//! ## Benchmark Categories
//! 1. Dense Matrix Multiply - naive vs SIMD
//! 2. Vector Norm Computation - naive vs SIMD
//! 3. Batch Residual Computation - naive vs SIMD
//! 4. Dot Products and Reductions
//!
//! ## Architecture Notes
//! - x86_64: AVX2 (256-bit, f32x8) or AVX-512 (512-bit, f32x16)
//! - aarch64: NEON (128-bit, f32x4)
//! - WASM: SIMD128 (128-bit)
use criterion::{
black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput,
};
use std::collections::hash_map::DefaultHasher;
use std::hash::{Hash, Hasher};
// ============================================================================
// TEST DATA GENERATION
// ============================================================================
fn generate_vec(len: usize, seed: u64) -> Vec<f32> {
(0..len)
.map(|i| {
let mut hasher = DefaultHasher::new();
(seed, i).hash(&mut hasher);
(hasher.finish() % 1000) as f32 / 1000.0 - 0.5
})
.collect()
}
fn generate_matrix(rows: usize, cols: usize, seed: u64) -> Vec<f32> {
(0..rows * cols)
.map(|i| {
let mut hasher = DefaultHasher::new();
(seed, i).hash(&mut hasher);
(hasher.finish() % 1000) as f32 / 1000.0 - 0.5
})
.collect()
}
// ============================================================================
// NAIVE IMPLEMENTATIONS (BASELINE)
// ============================================================================
/// Naive matrix-vector multiply: y = Ax
#[inline(never)]
fn matmul_naive(matrix: &[f32], x: &[f32], y: &mut [f32], rows: usize, cols: usize) {
for i in 0..rows {
let mut sum = 0.0f32;
let row_start = i * cols;
for j in 0..cols {
sum += matrix[row_start + j] * x[j];
}
y[i] = sum;
}
}
/// Naive squared norm: |v|^2
#[inline(never)]
fn norm_sq_naive(v: &[f32]) -> f32 {
let mut sum = 0.0f32;
for &x in v {
sum += x * x;
}
sum
}
/// Naive dot product: a . b
#[inline(never)]
fn dot_naive(a: &[f32], b: &[f32]) -> f32 {
let mut sum = 0.0f32;
for i in 0..a.len() {
sum += a[i] * b[i];
}
sum
}
/// Naive residual norm: |a - b|^2
#[inline(never)]
fn residual_norm_naive(a: &[f32], b: &[f32]) -> f32 {
let mut sum = 0.0f32;
for i in 0..a.len() {
let diff = a[i] - b[i];
sum += diff * diff;
}
sum
}
/// Naive batch residual computation
#[inline(never)]
fn batch_residual_naive(sources: &[Vec<f32>], targets: &[Vec<f32>]) -> f32 {
let mut total = 0.0f32;
for (src, tgt) in sources.iter().zip(targets.iter()) {
total += residual_norm_naive(src, tgt);
}
total
}
// ============================================================================
// SIMD-FRIENDLY IMPLEMENTATIONS
// ============================================================================
/// Unrolled matrix-vector multiply (auto-vectorization friendly)
#[inline(never)]
fn matmul_unrolled(matrix: &[f32], x: &[f32], y: &mut [f32], rows: usize, cols: usize) {
for i in 0..rows {
let row_start = i * cols;
// Process in chunks of 8
let chunks = cols / 8;
let mut acc0 = 0.0f32;
let mut acc1 = 0.0f32;
let mut acc2 = 0.0f32;
let mut acc3 = 0.0f32;
let mut acc4 = 0.0f32;
let mut acc5 = 0.0f32;
let mut acc6 = 0.0f32;
let mut acc7 = 0.0f32;
for c in 0..chunks {
let base = row_start + c * 8;
acc0 += matrix[base] * x[c * 8];
acc1 += matrix[base + 1] * x[c * 8 + 1];
acc2 += matrix[base + 2] * x[c * 8 + 2];
acc3 += matrix[base + 3] * x[c * 8 + 3];
acc4 += matrix[base + 4] * x[c * 8 + 4];
acc5 += matrix[base + 5] * x[c * 8 + 5];
acc6 += matrix[base + 6] * x[c * 8 + 6];
acc7 += matrix[base + 7] * x[c * 8 + 7];
}
let mut sum = acc0 + acc1 + acc2 + acc3 + acc4 + acc5 + acc6 + acc7;
// Handle remainder
for j in (chunks * 8)..cols {
sum += matrix[row_start + j] * x[j];
}
y[i] = sum;
}
}
/// Unrolled squared norm with 4 accumulators
#[inline(never)]
fn norm_sq_unrolled(v: &[f32]) -> f32 {
let chunks = v.chunks_exact(4);
let remainder = chunks.remainder();
let mut acc0 = 0.0f32;
let mut acc1 = 0.0f32;
let mut acc2 = 0.0f32;
let mut acc3 = 0.0f32;
for chunk in chunks {
acc0 += chunk[0] * chunk[0];
acc1 += chunk[1] * chunk[1];
acc2 += chunk[2] * chunk[2];
acc3 += chunk[3] * chunk[3];
}
let mut sum = acc0 + acc1 + acc2 + acc3;
for &x in remainder {
sum += x * x;
}
sum
}
/// Unrolled squared norm with 8 accumulators (better for wider SIMD)
#[inline(never)]
fn norm_sq_unrolled_8(v: &[f32]) -> f32 {
let chunks = v.chunks_exact(8);
let remainder = chunks.remainder();
let mut acc = [0.0f32; 8];
for chunk in chunks {
acc[0] += chunk[0] * chunk[0];
acc[1] += chunk[1] * chunk[1];
acc[2] += chunk[2] * chunk[2];
acc[3] += chunk[3] * chunk[3];
acc[4] += chunk[4] * chunk[4];
acc[5] += chunk[5] * chunk[5];
acc[6] += chunk[6] * chunk[6];
acc[7] += chunk[7] * chunk[7];
}
let mut sum: f32 = acc.iter().sum();
for &x in remainder {
sum += x * x;
}
sum
}
/// Iterator-based squared norm (relies on auto-vectorization)
#[inline(never)]
fn norm_sq_iter(v: &[f32]) -> f32 {
v.iter().map(|x| x * x).sum()
}
/// Unrolled dot product
#[inline(never)]
fn dot_unrolled(a: &[f32], b: &[f32]) -> f32 {
let chunks_a = a.chunks_exact(4);
let chunks_b = b.chunks_exact(4);
let rem_a = chunks_a.remainder();
let rem_b = chunks_b.remainder();
let mut acc0 = 0.0f32;
let mut acc1 = 0.0f32;
let mut acc2 = 0.0f32;
let mut acc3 = 0.0f32;
for (ca, cb) in chunks_a.zip(chunks_b) {
acc0 += ca[0] * cb[0];
acc1 += ca[1] * cb[1];
acc2 += ca[2] * cb[2];
acc3 += ca[3] * cb[3];
}
let mut sum = acc0 + acc1 + acc2 + acc3;
for (&a, &b) in rem_a.iter().zip(rem_b.iter()) {
sum += a * b;
}
sum
}
/// Unrolled residual norm
#[inline(never)]
fn residual_norm_unrolled(a: &[f32], b: &[f32]) -> f32 {
let chunks_a = a.chunks_exact(4);
let chunks_b = b.chunks_exact(4);
let rem_a = chunks_a.remainder();
let rem_b = chunks_b.remainder();
let mut acc0 = 0.0f32;
let mut acc1 = 0.0f32;
let mut acc2 = 0.0f32;
let mut acc3 = 0.0f32;
for (ca, cb) in chunks_a.zip(chunks_b) {
let d0 = ca[0] - cb[0];
let d1 = ca[1] - cb[1];
let d2 = ca[2] - cb[2];
let d3 = ca[3] - cb[3];
acc0 += d0 * d0;
acc1 += d1 * d1;
acc2 += d2 * d2;
acc3 += d3 * d3;
}
let mut sum = acc0 + acc1 + acc2 + acc3;
for (&a, &b) in rem_a.iter().zip(rem_b.iter()) {
let d = a - b;
sum += d * d;
}
sum
}
/// Batch residual with unrolled inner loop
#[inline(never)]
fn batch_residual_unrolled(sources: &[Vec<f32>], targets: &[Vec<f32>]) -> f32 {
let mut total = 0.0f32;
for (src, tgt) in sources.iter().zip(targets.iter()) {
total += residual_norm_unrolled(src, tgt);
}
total
}
// ============================================================================
// EXPLICIT SIMD (when wide crate is available)
// ============================================================================
#[cfg(feature = "simd")]
mod simd_impl {
use wide::f32x8;
/// SIMD squared norm using f32x8
#[inline(never)]
pub fn norm_sq_simd(v: &[f32]) -> f32 {
let chunks = v.chunks_exact(8);
let remainder = chunks.remainder();
let mut acc = f32x8::ZERO;
for chunk in chunks {
let vals = f32x8::from(<[f32; 8]>::try_from(chunk).unwrap());
acc += vals * vals;
}
let mut sum: f32 = acc.reduce_add();
for &x in remainder {
sum += x * x;
}
sum
}
/// SIMD dot product using f32x8
#[inline(never)]
pub fn dot_simd(a: &[f32], b: &[f32]) -> f32 {
let chunks_a = a.chunks_exact(8);
let chunks_b = b.chunks_exact(8);
let rem_a = chunks_a.remainder();
let rem_b = chunks_b.remainder();
let mut acc = f32x8::ZERO;
for (ca, cb) in chunks_a.zip(chunks_b) {
let va = f32x8::from(<[f32; 8]>::try_from(ca).unwrap());
let vb = f32x8::from(<[f32; 8]>::try_from(cb).unwrap());
acc += va * vb;
}
let mut sum: f32 = acc.reduce_add();
for (&a, &b) in rem_a.iter().zip(rem_b.iter()) {
sum += a * b;
}
sum
}
/// SIMD residual norm using f32x8
#[inline(never)]
pub fn residual_norm_simd(a: &[f32], b: &[f32]) -> f32 {
let chunks_a = a.chunks_exact(8);
let chunks_b = b.chunks_exact(8);
let rem_a = chunks_a.remainder();
let rem_b = chunks_b.remainder();
let mut acc = f32x8::ZERO;
for (ca, cb) in chunks_a.zip(chunks_b) {
let va = f32x8::from(<[f32; 8]>::try_from(ca).unwrap());
let vb = f32x8::from(<[f32; 8]>::try_from(cb).unwrap());
let diff = va - vb;
acc += diff * diff;
}
let mut sum: f32 = acc.reduce_add();
for (&a, &b) in rem_a.iter().zip(rem_b.iter()) {
let d = a - b;
sum += d * d;
}
sum
}
/// SIMD matrix-vector multiply
#[inline(never)]
pub fn matmul_simd(matrix: &[f32], x: &[f32], y: &mut [f32], rows: usize, cols: usize) {
for i in 0..rows {
let row_start = i * cols;
let row = &matrix[row_start..row_start + cols];
let chunks_m = row.chunks_exact(8);
let chunks_x = x.chunks_exact(8);
let rem_m = chunks_m.remainder();
let rem_x = chunks_x.remainder();
let mut acc = f32x8::ZERO;
for (cm, cx) in chunks_m.zip(chunks_x) {
let vm = f32x8::from(<[f32; 8]>::try_from(cm).unwrap());
let vx = f32x8::from(<[f32; 8]>::try_from(cx).unwrap());
acc += vm * vx;
}
let mut sum: f32 = acc.reduce_add();
for (&m, &xv) in rem_m.iter().zip(rem_x.iter()) {
sum += m * xv;
}
y[i] = sum;
}
}
/// SIMD batch residual
#[inline(never)]
pub fn batch_residual_simd(sources: &[Vec<f32>], targets: &[Vec<f32>]) -> f32 {
let mut total = 0.0f32;
for (src, tgt) in sources.iter().zip(targets.iter()) {
total += residual_norm_simd(src, tgt);
}
total
}
}
// ============================================================================
// DENSE MATRIX MULTIPLY BENCHMARKS
// ============================================================================
fn bench_dense_matmul(c: &mut Criterion) {
let mut group = c.benchmark_group("simd_matmul");
// Test matrix sizes: 64x64, 128x128, 256x256
for size in [64, 128, 256] {
let matrix = generate_matrix(size, size, 42);
let x = generate_vec(size, 123);
let mut y = vec![0.0f32; size];
group.throughput(Throughput::Elements((size * size) as u64));
group.bench_with_input(BenchmarkId::new("naive", size), &size, |b, _| {
b.iter(|| {
matmul_naive(
black_box(&matrix),
black_box(&x),
&mut y,
size,
size,
);
black_box(y[0])
})
});
group.bench_with_input(BenchmarkId::new("unrolled", size), &size, |b, _| {
b.iter(|| {
matmul_unrolled(
black_box(&matrix),
black_box(&x),
&mut y,
size,
size,
);
black_box(y[0])
})
});
#[cfg(feature = "simd")]
group.bench_with_input(BenchmarkId::new("simd", size), &size, |b, _| {
b.iter(|| {
simd_impl::matmul_simd(
black_box(&matrix),
black_box(&x),
&mut y,
size,
size,
);
black_box(y[0])
})
});
}
group.finish();
}
/// Benchmark non-square matrix multiply (projection)
fn bench_projection_matmul(c: &mut Criterion) {
let mut group = c.benchmark_group("simd_matmul_projection");
// Common projection sizes in coherence: 64->32, 128->64, 256->128
for (in_dim, out_dim) in [(64, 32), (128, 64), (256, 128)] {
let matrix = generate_matrix(out_dim, in_dim, 42);
let x = generate_vec(in_dim, 123);
let mut y = vec![0.0f32; out_dim];
group.throughput(Throughput::Elements((out_dim * in_dim) as u64));
group.bench_with_input(
BenchmarkId::new("naive", format!("{}x{}", in_dim, out_dim)),
&(in_dim, out_dim),
|b, _| {
b.iter(|| {
matmul_naive(
black_box(&matrix),
black_box(&x),
&mut y,
out_dim,
in_dim,
);
black_box(y[0])
})
},
);
group.bench_with_input(
BenchmarkId::new("unrolled", format!("{}x{}", in_dim, out_dim)),
&(in_dim, out_dim),
|b, _| {
b.iter(|| {
matmul_unrolled(
black_box(&matrix),
black_box(&x),
&mut y,
out_dim,
in_dim,
);
black_box(y[0])
})
},
);
#[cfg(feature = "simd")]
group.bench_with_input(
BenchmarkId::new("simd", format!("{}x{}", in_dim, out_dim)),
&(in_dim, out_dim),
|b, _| {
b.iter(|| {
simd_impl::matmul_simd(
black_box(&matrix),
black_box(&x),
&mut y,
out_dim,
in_dim,
);
black_box(y[0])
})
},
);
}
group.finish();
}
// ============================================================================
// NORM COMPUTATION BENCHMARKS
// ============================================================================
fn bench_norm_computation(c: &mut Criterion) {
let mut group = c.benchmark_group("simd_norm");
// Test dimensions aligned for SIMD
for dim in [64, 128, 256, 512, 1024] {
let v = generate_vec(dim, 42);
group.throughput(Throughput::Elements(dim as u64));
group.bench_with_input(BenchmarkId::new("naive", dim), &dim, |b, _| {
b.iter(|| black_box(norm_sq_naive(black_box(&v))))
});
group.bench_with_input(BenchmarkId::new("iter", dim), &dim, |b, _| {
b.iter(|| black_box(norm_sq_iter(black_box(&v))))
});
group.bench_with_input(BenchmarkId::new("unrolled_4", dim), &dim, |b, _| {
b.iter(|| black_box(norm_sq_unrolled(black_box(&v))))
});
group.bench_with_input(BenchmarkId::new("unrolled_8", dim), &dim, |b, _| {
b.iter(|| black_box(norm_sq_unrolled_8(black_box(&v))))
});
#[cfg(feature = "simd")]
group.bench_with_input(BenchmarkId::new("simd_f32x8", dim), &dim, |b, _| {
b.iter(|| black_box(simd_impl::norm_sq_simd(black_box(&v))))
});
}
group.finish();
}
// ============================================================================
// DOT PRODUCT BENCHMARKS
// ============================================================================
fn bench_dot_product(c: &mut Criterion) {
let mut group = c.benchmark_group("simd_dot");
for dim in [64, 256, 1024] {
let a = generate_vec(dim, 42);
let b = generate_vec(dim, 123);
group.throughput(Throughput::Elements(dim as u64));
group.bench_with_input(BenchmarkId::new("naive", dim), &dim, |b_iter, _| {
b_iter.iter(|| black_box(dot_naive(black_box(&a), black_box(&b))))
});
group.bench_with_input(BenchmarkId::new("unrolled", dim), &dim, |b_iter, _| {
b_iter.iter(|| black_box(dot_unrolled(black_box(&a), black_box(&b))))
});
#[cfg(feature = "simd")]
group.bench_with_input(BenchmarkId::new("simd", dim), &dim, |b_iter, _| {
b_iter.iter(|| black_box(simd_impl::dot_simd(black_box(&a), black_box(&b))))
});
}
group.finish();
}
// ============================================================================
// RESIDUAL NORM BENCHMARKS (CORE COHERENCE OPERATION)
// ============================================================================
fn bench_residual_norm(c: &mut Criterion) {
let mut group = c.benchmark_group("simd_residual_norm");
for dim in [64, 256, 1024] {
let a = generate_vec(dim, 42);
let b = generate_vec(dim, 123);
group.throughput(Throughput::Elements(dim as u64));
group.bench_with_input(BenchmarkId::new("naive", dim), &dim, |b_iter, _| {
b_iter.iter(|| black_box(residual_norm_naive(black_box(&a), black_box(&b))))
});
group.bench_with_input(BenchmarkId::new("unrolled", dim), &dim, |b_iter, _| {
b_iter.iter(|| black_box(residual_norm_unrolled(black_box(&a), black_box(&b))))
});
#[cfg(feature = "simd")]
group.bench_with_input(BenchmarkId::new("simd", dim), &dim, |b_iter, _| {
b_iter.iter(|| black_box(simd_impl::residual_norm_simd(black_box(&a), black_box(&b))))
});
}
group.finish();
}
// ============================================================================
// BATCH RESIDUAL BENCHMARKS
// ============================================================================
fn bench_batch_residual(c: &mut Criterion) {
let mut group = c.benchmark_group("simd_batch_residual");
let dim = 64;
for batch_size in [100, 1000, 10000] {
let sources: Vec<Vec<f32>> = (0..batch_size)
.map(|i| generate_vec(dim, i as u64))
.collect();
let targets: Vec<Vec<f32>> = (0..batch_size)
.map(|i| generate_vec(dim, i as u64 + 10000))
.collect();
group.throughput(Throughput::Elements(batch_size as u64));
group.bench_with_input(
BenchmarkId::new("naive", batch_size),
&batch_size,
|b, _| {
b.iter(|| black_box(batch_residual_naive(black_box(&sources), black_box(&targets))))
},
);
group.bench_with_input(
BenchmarkId::new("unrolled", batch_size),
&batch_size,
|b, _| {
b.iter(|| {
black_box(batch_residual_unrolled(
black_box(&sources),
black_box(&targets),
))
})
},
);
#[cfg(feature = "simd")]
group.bench_with_input(BenchmarkId::new("simd", batch_size), &batch_size, |b, _| {
b.iter(|| {
black_box(simd_impl::batch_residual_simd(
black_box(&sources),
black_box(&targets),
))
})
});
}
group.finish();
}
// ============================================================================
// MEMORY ALIGNMENT BENCHMARKS
// ============================================================================
fn bench_alignment_impact(c: &mut Criterion) {
let mut group = c.benchmark_group("simd_alignment");
let dim = 256;
// Aligned (multiple of 8)
{
let v = generate_vec(dim, 42);
group.bench_function("aligned_256", |b| {
b.iter(|| black_box(norm_sq_unrolled_8(black_box(&v))))
});
}
// Misaligned (not multiple of 8)
{
let v = generate_vec(dim + 3, 42);
group.bench_function("misaligned_259", |b| {
b.iter(|| black_box(norm_sq_unrolled_8(black_box(&v))))
});
}
// Small vector (below SIMD threshold)
{
let v = generate_vec(7, 42);
group.bench_function("small_7", |b| {
b.iter(|| black_box(norm_sq_unrolled_8(black_box(&v))))
});
}
group.finish();
}
// ============================================================================
// THROUGHPUT SCALING BENCHMARKS
// ============================================================================
fn bench_throughput_scaling(c: &mut Criterion) {
let mut group = c.benchmark_group("simd_throughput_scaling");
// Test how throughput scales with vector size
let sizes = [16, 32, 64, 128, 256, 512, 1024, 2048, 4096];
for &size in &sizes {
let a = generate_vec(size, 42);
let b = generate_vec(size, 123);
group.throughput(Throughput::Bytes((size * 4 * 2) as u64)); // 2 vectors, 4 bytes each
group.bench_with_input(
BenchmarkId::new("residual_unrolled", size),
&size,
|bench, _| {
bench.iter(|| black_box(residual_norm_unrolled(black_box(&a), black_box(&b))))
},
);
#[cfg(feature = "simd")]
group.bench_with_input(BenchmarkId::new("residual_simd", size), &size, |bench, _| {
bench.iter(|| black_box(simd_impl::residual_norm_simd(black_box(&a), black_box(&b))))
});
}
group.finish();
}
// ============================================================================
// COHERENCE-SPECIFIC SIMD PATTERNS
// ============================================================================
/// Fused multiply-add pattern for coherence energy
fn bench_fma_pattern(c: &mut Criterion) {
let mut group = c.benchmark_group("simd_fma_pattern");
let dim = 256;
let a = generate_vec(dim, 42);
let b = generate_vec(dim, 123);
let weight = 1.5f32;
// Without FMA (separate multiply and add)
group.bench_function("separate_ops", |bench| {
bench.iter(|| {
let mut sum = 0.0f32;
for i in 0..dim {
let diff = a[i] - b[i];
let sq = diff * diff;
sum += sq;
}
black_box(weight * sum)
})
});
// With potential FMA (compiler may optimize)
group.bench_function("fma_friendly", |bench| {
bench.iter(|| {
let mut acc0 = 0.0f32;
let mut acc1 = 0.0f32;
let mut acc2 = 0.0f32;
let mut acc3 = 0.0f32;
let chunks = dim / 4;
for c in 0..chunks {
let base = c * 4;
let d0 = a[base] - b[base];
let d1 = a[base + 1] - b[base + 1];
let d2 = a[base + 2] - b[base + 2];
let d3 = a[base + 3] - b[base + 3];
// These can become FMA operations
acc0 = d0.mul_add(d0, acc0);
acc1 = d1.mul_add(d1, acc1);
acc2 = d2.mul_add(d2, acc2);
acc3 = d3.mul_add(d3, acc3);
}
black_box(weight * (acc0 + acc1 + acc2 + acc3))
})
});
group.finish();
}
// ============================================================================
// CRITERION CONFIGURATION
// ============================================================================
criterion_group!(
matmul_benches,
bench_dense_matmul,
bench_projection_matmul,
);
criterion_group!(
vector_ops_benches,
bench_norm_computation,
bench_dot_product,
bench_residual_norm,
);
criterion_group!(
batch_benches,
bench_batch_residual,
);
criterion_group!(
optimization_benches,
bench_alignment_impact,
bench_throughput_scaling,
bench_fma_pattern,
);
criterion_main!(
matmul_benches,
vector_ops_benches,
batch_benches,
optimization_benches
);

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//! GPU Buffer Management
//!
//! Provides efficient GPU buffer allocation, management, and data transfer
//! for the coherence engine. Implements a buffer pool for reuse and
//! minimizes CPU-GPU synchronization overhead.
use super::error::{GpuError, GpuResult};
use bytemuck::{Pod, Zeroable};
use std::collections::HashMap;
use std::sync::Arc;
use wgpu::{Buffer, BufferDescriptor, BufferUsages, Device, Queue};
/// Buffer usage flags for coherence computation
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum BufferUsage {
/// Storage buffer for node states
NodeStates,
/// Storage buffer for edge data
EdgeData,
/// Storage buffer for restriction maps
RestrictionMaps,
/// Storage buffer for residuals
Residuals,
/// Storage buffer for energy values
Energies,
/// Storage buffer for attention weights
AttentionWeights,
/// Storage buffer for routing decisions
RoutingDecisions,
/// Uniform buffer for shader parameters
Uniforms,
/// Staging buffer for CPU readback
Staging,
}
/// GPU-side node state representation
#[repr(C)]
#[derive(Debug, Clone, Copy, Pod, Zeroable)]
pub struct GpuNodeState {
/// Flattened state vector (padded to MAX_STATE_DIM)
pub state: [f32; 128], // Will be dynamically sized based on actual dim
/// Actual dimension of the state vector
pub dim: u32,
/// Node index
pub index: u32,
/// Padding for alignment
pub _padding: [u32; 2],
}
/// GPU-side edge representation
#[repr(C)]
#[derive(Debug, Clone, Copy, Pod, Zeroable)]
pub struct GpuEdge {
/// Source node index
pub source_idx: u32,
/// Target node index
pub target_idx: u32,
/// Edge weight
pub weight: f32,
/// Restriction map index for source
pub rho_source_idx: u32,
/// Restriction map index for target
pub rho_target_idx: u32,
/// Output dimension of restriction maps
pub comparison_dim: u32,
/// Padding for alignment
pub _padding: [u32; 2],
}
/// GPU-side restriction map (dense matrix stored row-major)
#[repr(C)]
#[derive(Debug, Clone, Copy, Pod, Zeroable)]
pub struct GpuRestrictionMap {
/// Matrix type: 0=identity, 1=diagonal, 2=projection, 3=dense
pub map_type: u32,
/// Input dimension
pub input_dim: u32,
/// Output dimension
pub output_dim: u32,
/// Offset into the shared data buffer
pub data_offset: u32,
/// Number of elements in data
pub data_len: u32,
/// Padding for alignment
pub _padding: [u32; 3],
}
/// GPU-side shader parameters
#[repr(C)]
#[derive(Debug, Clone, Copy, Pod, Zeroable)]
pub struct GpuParams {
/// Number of edges
pub num_edges: u32,
/// Number of nodes
pub num_nodes: u32,
/// State dimension
pub state_dim: u32,
/// Beta parameter for attention
pub beta: f32,
/// Lane 0 threshold (reflex)
pub threshold_lane0: f32,
/// Lane 1 threshold (retrieval)
pub threshold_lane1: f32,
/// Lane 2 threshold (heavy)
pub threshold_lane2: f32,
/// Padding for alignment
pub _padding: u32,
}
/// Wrapper around a wgpu Buffer with metadata
pub struct GpuBuffer {
/// The underlying wgpu buffer
pub buffer: Buffer,
/// Size in bytes
pub size: usize,
/// Usage flags
pub usage: BufferUsage,
/// Label for debugging
pub label: String,
}
impl GpuBuffer {
/// Create a new GPU buffer
pub fn new(
device: &Device,
size: usize,
usage: BufferUsage,
label: impl Into<String>,
) -> GpuResult<Self> {
let label = label.into();
let wgpu_usage = Self::to_wgpu_usage(usage);
let buffer = device.create_buffer(&BufferDescriptor {
label: Some(&label),
size: size as u64,
usage: wgpu_usage,
mapped_at_creation: false,
});
Ok(Self {
buffer,
size,
usage,
label,
})
}
/// Create a new GPU buffer with initial data
pub fn new_with_data<T: Pod>(
device: &Device,
queue: &Queue,
data: &[T],
usage: BufferUsage,
label: impl Into<String>,
) -> GpuResult<Self> {
let label = label.into();
let bytes = bytemuck::cast_slice(data);
let size = bytes.len();
let wgpu_usage = Self::to_wgpu_usage(usage);
let buffer = device.create_buffer(&BufferDescriptor {
label: Some(&label),
size: size as u64,
usage: wgpu_usage,
mapped_at_creation: false,
});
queue.write_buffer(&buffer, 0, bytes);
Ok(Self {
buffer,
size,
usage,
label,
})
}
/// Write data to the buffer
pub fn write<T: Pod>(&self, queue: &Queue, data: &[T]) -> GpuResult<()> {
let bytes = bytemuck::cast_slice(data);
if bytes.len() > self.size {
return Err(GpuError::BufferSizeMismatch {
expected: self.size,
actual: bytes.len(),
});
}
queue.write_buffer(&self.buffer, 0, bytes);
Ok(())
}
/// Convert our usage to wgpu usage flags
fn to_wgpu_usage(usage: BufferUsage) -> BufferUsages {
match usage {
BufferUsage::NodeStates
| BufferUsage::EdgeData
| BufferUsage::RestrictionMaps
| BufferUsage::Residuals
| BufferUsage::Energies
| BufferUsage::AttentionWeights
| BufferUsage::RoutingDecisions => {
BufferUsages::STORAGE | BufferUsages::COPY_SRC | BufferUsages::COPY_DST
}
BufferUsage::Uniforms => BufferUsages::UNIFORM | BufferUsages::COPY_DST,
BufferUsage::Staging => BufferUsages::MAP_READ | BufferUsages::COPY_DST,
}
}
}
/// Buffer manager for efficient allocation and reuse
pub struct GpuBufferManager {
device: Arc<Device>,
queue: Arc<Queue>,
/// Buffer pool keyed by (usage, size_bucket)
pool: HashMap<(BufferUsage, usize), Vec<GpuBuffer>>,
/// Active buffers currently in use
active: HashMap<String, GpuBuffer>,
}
impl GpuBufferManager {
/// Create a new buffer manager
pub fn new(device: Arc<Device>, queue: Arc<Queue>) -> Self {
Self {
device,
queue,
pool: HashMap::new(),
active: HashMap::new(),
}
}
/// Allocate or reuse a buffer
pub fn allocate(
&mut self,
size: usize,
usage: BufferUsage,
label: impl Into<String>,
) -> GpuResult<&GpuBuffer> {
let label = label.into();
let bucket = Self::size_bucket(size);
// Try to reuse from pool
if let Some(buffers) = self.pool.get_mut(&(usage, bucket)) {
if let Some(buffer) = buffers.pop() {
self.active.insert(label.clone(), buffer);
return Ok(self.active.get(&label).unwrap());
}
}
// Allocate new buffer
let buffer = GpuBuffer::new(&self.device, bucket, usage, &label)?;
self.active.insert(label.clone(), buffer);
Ok(self.active.get(&label).unwrap())
}
/// Allocate or reuse a buffer with initial data
pub fn allocate_with_data<T: Pod>(
&mut self,
data: &[T],
usage: BufferUsage,
label: impl Into<String>,
) -> GpuResult<&GpuBuffer> {
let label = label.into();
let size = std::mem::size_of_val(data);
let bucket = Self::size_bucket(size);
// Try to reuse from pool
if let Some(buffers) = self.pool.get_mut(&(usage, bucket)) {
if let Some(buffer) = buffers.pop() {
buffer.write(&self.queue, data)?;
self.active.insert(label.clone(), buffer);
return Ok(self.active.get(&label).unwrap());
}
}
// Allocate new buffer with data
let buffer = GpuBuffer::new_with_data(&self.device, &self.queue, data, usage, &label)?;
self.active.insert(label.clone(), buffer);
Ok(self.active.get(&label).unwrap())
}
/// Get an active buffer by label
pub fn get(&self, label: &str) -> Option<&GpuBuffer> {
self.active.get(label)
}
/// Release a buffer back to the pool for reuse
pub fn release(&mut self, label: &str) {
if let Some(buffer) = self.active.remove(label) {
let bucket = Self::size_bucket(buffer.size);
self.pool
.entry((buffer.usage, bucket))
.or_default()
.push(buffer);
}
}
/// Release all active buffers back to the pool
pub fn release_all(&mut self) {
let labels: Vec<_> = self.active.keys().cloned().collect();
for label in labels {
self.release(&label);
}
}
/// Clear all buffers (both pool and active)
pub fn clear(&mut self) {
self.active.clear();
self.pool.clear();
}
/// Round size up to nearest power of 2 for efficient reuse
fn size_bucket(size: usize) -> usize {
const MIN_BUCKET: usize = 256;
if size <= MIN_BUCKET {
MIN_BUCKET
} else {
size.next_power_of_two()
}
}
/// Get the underlying device
pub fn device(&self) -> &Device {
&self.device
}
/// Get the underlying queue
pub fn queue(&self) -> &Queue {
&self.queue
}
}
// ============================================================================
// BUFFER USAGE FLAGS (for pipeline.rs compatibility)
// ============================================================================
/// Buffer usage flags for flexible configuration
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub struct BufferUsageFlags {
/// Can be read from GPU (STORAGE)
pub storage_read: bool,
/// Can be written to by GPU (STORAGE)
pub storage_write: bool,
/// Can be used as uniform buffer
pub uniform: bool,
/// Can be mapped for CPU read
pub map_read: bool,
/// Can be mapped for CPU write
pub map_write: bool,
/// Can be used as copy source
pub copy_src: bool,
/// Can be used as copy destination
pub copy_dst: bool,
/// Can be used for indirect dispatch
pub indirect: bool,
}
impl BufferUsageFlags {
/// Storage buffer (read-only)
pub const fn storage_readonly() -> Self {
Self {
storage_read: true,
storage_write: false,
uniform: false,
map_read: false,
map_write: false,
copy_src: true,
copy_dst: true,
indirect: false,
}
}
/// Storage buffer (read-write)
pub const fn storage_readwrite() -> Self {
Self {
storage_read: true,
storage_write: true,
uniform: false,
map_read: false,
map_write: false,
copy_src: true,
copy_dst: true,
indirect: false,
}
}
/// Uniform buffer
pub const fn uniform() -> Self {
Self {
storage_read: false,
storage_write: false,
uniform: true,
map_read: false,
map_write: false,
copy_src: false,
copy_dst: true,
indirect: false,
}
}
/// Staging buffer for read-back
pub const fn staging_read() -> Self {
Self {
storage_read: false,
storage_write: false,
uniform: false,
map_read: true,
map_write: false,
copy_src: false,
copy_dst: true,
indirect: false,
}
}
/// Staging buffer for upload
pub const fn staging_write() -> Self {
Self {
storage_read: false,
storage_write: false,
uniform: false,
map_read: false,
map_write: true,
copy_src: true,
copy_dst: false,
indirect: false,
}
}
/// Indirect dispatch buffer
pub const fn indirect() -> Self {
Self {
storage_read: true,
storage_write: true,
uniform: false,
map_read: false,
map_write: false,
copy_src: true,
copy_dst: true,
indirect: true,
}
}
/// Convert to wgpu buffer usages
pub fn to_wgpu(&self) -> BufferUsages {
let mut usages = BufferUsages::empty();
if self.storage_read || self.storage_write {
usages |= BufferUsages::STORAGE;
}
if self.uniform {
usages |= BufferUsages::UNIFORM;
}
if self.map_read {
usages |= BufferUsages::MAP_READ;
}
if self.map_write {
usages |= BufferUsages::MAP_WRITE;
}
if self.copy_src {
usages |= BufferUsages::COPY_SRC;
}
if self.copy_dst {
usages |= BufferUsages::COPY_DST;
}
if self.indirect {
usages |= BufferUsages::INDIRECT;
}
usages
}
}
// ============================================================================
// BUFFER KEY AND POOL (for dispatch.rs compatibility)
// ============================================================================
/// Key for buffer pool lookups
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub struct BufferKey {
/// Buffer size in bytes
pub size: u64,
/// Buffer usage flags
pub usage: BufferUsageFlags,
}
impl BufferKey {
/// Create a new buffer key
pub fn new(size: u64, usage: BufferUsageFlags) -> Self {
Self { size, usage }
}
}
/// Buffer pool for reusing GPU allocations with DashMap for concurrent access
pub struct GpuBufferPool {
device: Arc<Device>,
buffers: dashmap::DashMap<BufferKey, Vec<GpuBuffer>>,
max_pool_size: usize,
}
impl GpuBufferPool {
/// Create a new buffer pool
pub fn new(device: Arc<Device>) -> Self {
Self::with_capacity(device, super::DEFAULT_POOL_CAPACITY)
}
/// Create a new buffer pool with custom capacity
pub fn with_capacity(device: Arc<Device>, max_pool_size: usize) -> Self {
Self {
device,
buffers: dashmap::DashMap::new(),
max_pool_size,
}
}
/// Acquire a buffer from the pool or create a new one.
pub fn acquire(&self, size: u64, usage: BufferUsageFlags) -> GpuResult<GpuBuffer> {
if size > super::MAX_BUFFER_SIZE {
return Err(GpuError::BufferTooLarge {
size,
max: super::MAX_BUFFER_SIZE,
});
}
let key = BufferKey::new(size, usage);
// Try to get from pool
if let Some(mut buffers) = self.buffers.get_mut(&key) {
if let Some(buffer) = buffers.pop() {
return Ok(buffer);
}
}
// Create new buffer
let wgpu_buffer = self.device.create_buffer(&BufferDescriptor {
label: Some("pooled_buffer"),
size,
usage: usage.to_wgpu(),
mapped_at_creation: false,
});
Ok(GpuBuffer {
buffer: wgpu_buffer,
size: size as usize,
usage: BufferUsage::Staging, // Default usage type
label: "pooled_buffer".to_string(),
})
}
/// Return a buffer to the pool for reuse.
pub fn release(&self, buffer: GpuBuffer) {
let size = buffer.size as u64;
let usage = BufferUsageFlags::storage_readwrite(); // Default
let key = BufferKey::new(size, usage);
let mut buffers = self.buffers.entry(key).or_insert_with(Vec::new);
if buffers.len() < self.max_pool_size {
buffers.push(buffer);
}
}
/// Clear all pooled buffers
pub fn clear(&self) {
self.buffers.clear();
}
/// Get statistics about the pool
pub fn stats(&self) -> PoolStats {
let mut total_buffers = 0;
let mut total_bytes = 0u64;
for entry in self.buffers.iter() {
total_buffers += entry.value().len();
total_bytes += entry.key().size * entry.value().len() as u64;
}
PoolStats {
total_buffers,
total_bytes,
bucket_count: self.buffers.len(),
}
}
}
/// Statistics about the buffer pool
#[derive(Debug, Clone)]
pub struct PoolStats {
/// Total number of pooled buffers
pub total_buffers: usize,
/// Total bytes allocated in pool
pub total_bytes: u64,
/// Number of unique buffer configurations
pub bucket_count: usize,
}
// ============================================================================
// EXTENDED GPUBUFFER METHODS (for pipeline.rs compatibility)
// ============================================================================
impl GpuBuffer {
/// Create a binding entry for this buffer.
pub fn binding(&self, binding: u32) -> wgpu::BindGroupEntry {
wgpu::BindGroupEntry {
binding,
resource: self.buffer.as_entire_binding(),
}
}
/// Get the underlying wgpu buffer
pub fn buffer(&self) -> &Buffer {
&self.buffer
}
/// Create a new storage buffer with initial data (for dispatch compatibility)
pub fn new_storage<T: Pod>(device: &Device, queue: &Queue, data: &[T], read_write: bool) -> GpuResult<Self> {
let usage = if read_write {
BufferUsage::Residuals
} else {
BufferUsage::NodeStates
};
Self::new_with_data(device, queue, data, usage, "storage_buffer")
}
/// Create a new uninitialized storage buffer
pub fn new_storage_uninit<T: Pod>(device: &Device, count: usize, read_write: bool) -> GpuResult<Self> {
let size = count * std::mem::size_of::<T>();
let usage = if read_write {
BufferUsage::Residuals
} else {
BufferUsage::NodeStates
};
Self::new(device, size, usage, "storage_buffer_uninit")
}
/// Create a new uniform buffer with data
pub fn new_uniform<T: Pod>(device: &Device, queue: &Queue, data: &T) -> GpuResult<Self> {
Self::new_with_data(device, queue, std::slice::from_ref(data), BufferUsage::Uniforms, "uniform_buffer")
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_size_bucket() {
assert_eq!(GpuBufferManager::size_bucket(100), 256);
assert_eq!(GpuBufferManager::size_bucket(256), 256);
assert_eq!(GpuBufferManager::size_bucket(257), 512);
assert_eq!(GpuBufferManager::size_bucket(1000), 1024);
}
#[test]
fn test_gpu_params_alignment() {
// Ensure our GPU structs are properly aligned for wgpu
assert_eq!(std::mem::size_of::<GpuParams>(), 32);
assert_eq!(std::mem::align_of::<GpuParams>(), 4);
}
#[test]
fn test_gpu_edge_alignment() {
assert_eq!(std::mem::size_of::<GpuEdge>(), 32);
assert_eq!(std::mem::align_of::<GpuEdge>(), 4);
}
#[test]
fn test_gpu_restriction_map_alignment() {
assert_eq!(std::mem::size_of::<GpuRestrictionMap>(), 32);
assert_eq!(std::mem::align_of::<GpuRestrictionMap>(), 4);
}
#[test]
fn test_buffer_usage_flags() {
let readonly = BufferUsageFlags::storage_readonly();
assert!(readonly.storage_read);
assert!(!readonly.storage_write);
let readwrite = BufferUsageFlags::storage_readwrite();
assert!(readwrite.storage_read);
assert!(readwrite.storage_write);
}
#[test]
fn test_buffer_key_equality() {
let key1 = BufferKey::new(1024, BufferUsageFlags::storage_readonly());
let key2 = BufferKey::new(1024, BufferUsageFlags::storage_readonly());
let key3 = BufferKey::new(2048, BufferUsageFlags::storage_readonly());
assert_eq!(key1, key2);
assert_ne!(key1, key3);
}
}

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crates/prime-radiant/src/gpu/device.rs Normal file
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//! GPU device initialization and management.
//!
//! This module provides the core GPU device abstraction using wgpu,
//! handling adapter selection, device creation, and queue management.
use std::sync::Arc;
use tracing::{debug, info, warn};
use wgpu::{Adapter, Device, Instance, Queue};
use super::error::{GpuError, GpuResult};
/// Information about the GPU device
#[derive(Debug, Clone)]
pub struct GpuDeviceInfo {
/// Device name
pub name: String,
/// Vendor ID
pub vendor: u32,
/// Device ID
pub device_id: u32,
/// Device type (discrete, integrated, etc.)
pub device_type: String,
/// Backend API (Vulkan, Metal, DX12, etc.)
pub backend: String,
/// Maximum buffer size
pub max_buffer_size: u64,
/// Maximum compute workgroup size per dimension
pub max_workgroup_size: [u32; 3],
/// Maximum compute workgroups per dimension
pub max_workgroups: [u32; 3],
/// Maximum storage buffers per shader stage
pub max_storage_buffers: u32,
}
/// GPU device wrapper providing access to wgpu resources
pub struct GpuDevice {
instance: Instance,
adapter: Adapter,
device: Arc<Device>,
queue: Arc<Queue>,
info: GpuDeviceInfo,
}
impl GpuDevice {
/// Create a new GPU device with default configuration.
///
/// This will:
/// 1. Create a wgpu instance with all available backends
/// 2. Request a high-performance adapter
/// 3. Create the device and queue
///
/// # Errors
///
/// Returns `GpuError::NoAdapter` if no suitable GPU is found.
/// Returns `GpuError::DeviceRequestFailed` if device creation fails.
pub async fn new() -> GpuResult<Self> {
Self::with_options(GpuDeviceOptions::default()).await
}
/// Create a new GPU device with custom options.
pub async fn with_options(options: GpuDeviceOptions) -> GpuResult<Self> {
let instance = Instance::new(wgpu::InstanceDescriptor {
backends: options.backends,
flags: wgpu::InstanceFlags::default(),
dx12_shader_compiler: wgpu::Dx12Compiler::default(),
gles_minor_version: wgpu::Gles3MinorVersion::default(),
});
debug!("Created wgpu instance with backends: {:?}", options.backends);
let adapter = instance
.request_adapter(&wgpu::RequestAdapterOptions {
power_preference: options.power_preference,
compatible_surface: None,
force_fallback_adapter: options.force_fallback,
})
.await
.ok_or(GpuError::NoAdapter)?;
let adapter_info = adapter.get_info();
info!(
"Selected GPU adapter: {} ({:?})",
adapter_info.name, adapter_info.backend
);
let limits = if options.use_downlevel_limits {
wgpu::Limits::downlevel_defaults()
} else {
wgpu::Limits::default()
};
let (device, queue) = adapter
.request_device(
&wgpu::DeviceDescriptor {
label: Some("prime-radiant-gpu"),
required_features: options.required_features,
required_limits: limits.clone(),
memory_hints: wgpu::MemoryHints::Performance,
},
None,
)
.await?;
// Set up error handling
device.on_uncaptured_error(Box::new(|error| {
warn!("Uncaptured GPU error: {:?}", error);
}));
let info = GpuDeviceInfo {
name: adapter_info.name.clone(),
vendor: adapter_info.vendor,
device_id: adapter_info.device,
device_type: format!("{:?}", adapter_info.device_type),
backend: format!("{:?}", adapter_info.backend),
max_buffer_size: limits.max_buffer_size as u64,
max_workgroup_size: [
limits.max_compute_workgroup_size_x,
limits.max_compute_workgroup_size_y,
limits.max_compute_workgroup_size_z,
],
max_workgroups: [
limits.max_compute_workgroups_per_dimension,
limits.max_compute_workgroups_per_dimension,
limits.max_compute_workgroups_per_dimension,
],
max_storage_buffers: limits.max_storage_buffers_per_shader_stage,
};
debug!("GPU device info: {:?}", info);
Ok(Self {
instance,
adapter,
device: Arc::new(device),
queue: Arc::new(queue),
info,
})
}
/// Get a reference to the wgpu device
pub fn device(&self) -> &Device {
&self.device
}
/// Get a shared reference to the wgpu device
pub fn device_arc(&self) -> Arc<Device> {
Arc::clone(&self.device)
}
/// Get a reference to the command queue
pub fn queue(&self) -> &Queue {
&self.queue
}
/// Get a shared reference to the command queue
pub fn queue_arc(&self) -> Arc<Queue> {
Arc::clone(&self.queue)
}
/// Get device information
pub fn info(&self) -> &GpuDeviceInfo {
&self.info
}
/// Get the wgpu instance
pub fn instance(&self) -> &Instance {
&self.instance
}
/// Get the wgpu adapter
pub fn adapter(&self) -> &Adapter {
&self.adapter
}
/// Check if a feature is supported
pub fn supports_feature(&self, feature: wgpu::Features) -> bool {
self.adapter.features().contains(feature)
}
/// Poll the device for completed work.
///
/// This is useful when you need to ensure GPU work has completed
/// before continuing on the CPU.
pub fn poll(&self, wait: bool) -> bool {
self.device.poll(if wait {
wgpu::Maintain::Wait
} else {
wgpu::Maintain::Poll
})
.is_queue_empty()
}
/// Submit a command buffer to the queue
pub fn submit(&self, command_buffer: wgpu::CommandBuffer) -> wgpu::SubmissionIndex {
self.queue.submit(std::iter::once(command_buffer))
}
/// Submit multiple command buffers to the queue
pub fn submit_multiple(
&self,
command_buffers: impl IntoIterator<Item = wgpu::CommandBuffer>,
) -> wgpu::SubmissionIndex {
self.queue.submit(command_buffers)
}
}
/// Options for GPU device creation
#[derive(Debug, Clone)]
pub struct GpuDeviceOptions {
/// Backends to use (default: all)
pub backends: wgpu::Backends,
/// Power preference (default: high performance)
pub power_preference: wgpu::PowerPreference,
/// Required GPU features
pub required_features: wgpu::Features,
/// Use downlevel limits for broader compatibility
pub use_downlevel_limits: bool,
/// Force fallback adapter (software rendering)
pub force_fallback: bool,
}
impl Default for GpuDeviceOptions {
fn default() -> Self {
Self {
backends: wgpu::Backends::all(),
power_preference: wgpu::PowerPreference::HighPerformance,
required_features: wgpu::Features::empty(),
use_downlevel_limits: false,
force_fallback: false,
}
}
}
impl GpuDeviceOptions {
/// Create options for low-power mode (integrated GPU preferred)
pub fn low_power() -> Self {
Self {
power_preference: wgpu::PowerPreference::LowPower,
..Default::default()
}
}
/// Create options for maximum compatibility
pub fn compatible() -> Self {
Self {
use_downlevel_limits: true,
..Default::default()
}
}
/// Create options for software fallback
pub fn software() -> Self {
Self {
force_fallback: true,
use_downlevel_limits: true,
..Default::default()
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_device_options_default() {
let options = GpuDeviceOptions::default();
assert_eq!(options.power_preference, wgpu::PowerPreference::HighPerformance);
assert!(!options.force_fallback);
}
#[test]
fn test_device_options_low_power() {
let options = GpuDeviceOptions::low_power();
assert_eq!(options.power_preference, wgpu::PowerPreference::LowPower);
}
#[test]
fn test_device_options_compatible() {
let options = GpuDeviceOptions::compatible();
assert!(options.use_downlevel_limits);
}
}

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crates/prime-radiant/src/gpu/dispatch.rs Normal file
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//! Kernel dispatch and synchronization for GPU compute operations.
//!
//! This module provides the dispatcher for executing compute kernels on the GPU,
//! including support for:
//! - Single kernel dispatch
//! - Indirect dispatch (workgroup count from GPU buffer)
//! - Chained dispatch for fused kernels
//! - Synchronization and timing
use std::sync::Arc;
use tracing::{debug, trace};
use wgpu::{CommandEncoder, Device, Queue};
use super::buffer::{GpuBuffer, GpuBufferPool};
use super::device::GpuDevice;
use super::error::{GpuError, GpuResult};
use super::pipeline::{ComputePipeline, PipelineCache};
/// Configuration for a dispatch operation
#[derive(Debug, Clone)]
pub struct DispatchConfig {
/// Label for debugging
pub label: Option<String>,
/// Whether to wait for completion
pub wait: bool,
/// Timeout in milliseconds (0 = no timeout)
pub timeout_ms: u64,
}
impl Default for DispatchConfig {
fn default() -> Self {
Self {
label: None,
wait: false,
timeout_ms: 0,
}
}
}
impl DispatchConfig {
/// Create a config that waits for completion
pub fn wait() -> Self {
Self {
wait: true,
..Default::default()
}
}
/// Create a config with a label
pub fn with_label(label: impl Into<String>) -> Self {
Self {
label: Some(label.into()),
..Default::default()
}
}
/// Set the timeout
pub fn with_timeout(mut self, timeout_ms: u64) -> Self {
self.timeout_ms = timeout_ms;
self
}
/// Set wait flag
pub fn with_wait(mut self, wait: bool) -> Self {
self.wait = wait;
self
}
}
/// GPU dispatcher for executing compute kernels
pub struct GpuDispatcher {
device: Arc<GpuDevice>,
pipeline_cache: PipelineCache,
buffer_pool: GpuBufferPool,
}
impl GpuDispatcher {
/// Create a new dispatcher
pub fn new(device: Arc<GpuDevice>) -> Self {
let pipeline_cache = PipelineCache::new(device.device_arc());
let buffer_pool = GpuBufferPool::new(device.device_arc());
Self {
device,
pipeline_cache,
buffer_pool,
}
}
/// Get the underlying GPU device
pub fn device(&self) -> &GpuDevice {
&self.device
}
/// Get the pipeline cache
pub fn pipeline_cache(&self) -> &PipelineCache {
&self.pipeline_cache
}
/// Get the buffer pool
pub fn buffer_pool(&self) -> &GpuBufferPool {
&self.buffer_pool
}
/// Dispatch a compute kernel.
///
/// # Arguments
///
/// * `pipeline` - The compute pipeline to execute
/// * `bind_group` - The bind group with buffer bindings
/// * `workgroups` - Number of workgroups [x, y, z]
///
/// # Example
///
/// ```rust,ignore
/// dispatcher.dispatch(&pipeline, &bind_group, [4, 1, 1]).await?;
/// ```
pub async fn dispatch(
&self,
pipeline: &ComputePipeline,
bind_group: &wgpu::BindGroup,
workgroups: [u32; 3],
) -> GpuResult<()> {
self.dispatch_with_config(pipeline, bind_group, workgroups, DispatchConfig::default())
.await
}
/// Dispatch with custom configuration.
pub async fn dispatch_with_config(
&self,
pipeline: &ComputePipeline,
bind_group: &wgpu::BindGroup,
workgroups: [u32; 3],
config: DispatchConfig,
) -> GpuResult<()> {
// Validate workgroup count
let limits = &self.device.info().max_workgroups;
if workgroups[0] > limits[0] || workgroups[1] > limits[1] || workgroups[2] > limits[2] {
return Err(GpuError::InvalidWorkgroupSize {
x: workgroups[0],
y: workgroups[1],
z: workgroups[2],
});
}
let label = config.label.as_deref().unwrap_or("dispatch");
debug!(
"Dispatching '{}' with workgroups [{}, {}, {}]",
label, workgroups[0], workgroups[1], workgroups[2]
);
let mut encoder = self
.device
.device()
.create_command_encoder(&wgpu::CommandEncoderDescriptor {
label: Some(label),
});
{
let mut pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
label: Some(label),
timestamp_writes: None,
});
pass.set_pipeline(pipeline.pipeline());
pass.set_bind_group(0, Some(bind_group), &[]);
pass.dispatch_workgroups(workgroups[0], workgroups[1], workgroups[2]);
}
self.device.submit(encoder.finish());
if config.wait {
self.device.poll(true);
}
Ok(())
}
/// Dispatch using indirect workgroup count from a buffer.
///
/// The indirect buffer must contain [x, y, z] workgroup counts as u32.
pub async fn dispatch_indirect(
&self,
pipeline: &ComputePipeline,
bind_group: &wgpu::BindGroup,
indirect_buffer: &GpuBuffer,
) -> GpuResult<()> {
self.dispatch_indirect_with_config(
pipeline,
bind_group,
indirect_buffer,
0,
DispatchConfig::default(),
)
.await
}
/// Dispatch indirect with offset and configuration.
pub async fn dispatch_indirect_with_config(
&self,
pipeline: &ComputePipeline,
bind_group: &wgpu::BindGroup,
indirect_buffer: &GpuBuffer,
indirect_offset: u64,
config: DispatchConfig,
) -> GpuResult<()> {
let label = config.label.as_deref().unwrap_or("dispatch_indirect");
debug!("Dispatching indirect '{}'", label);
let mut encoder = self
.device
.device()
.create_command_encoder(&wgpu::CommandEncoderDescriptor {
label: Some(label),
});
{
let mut pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
label: Some(label),
timestamp_writes: None,
});
pass.set_pipeline(pipeline.pipeline());
pass.set_bind_group(0, Some(bind_group), &[]);
pass.dispatch_workgroups_indirect(indirect_buffer.buffer(), indirect_offset);
}
self.device.submit(encoder.finish());
if config.wait {
self.device.poll(true);
}
Ok(())
}
/// Dispatch multiple kernels in a chain (fused execution).
///
/// All dispatches are recorded into a single command buffer for
/// optimal GPU utilization.
///
/// # Arguments
///
/// * `dispatches` - List of (pipeline, bind_group, workgroups) tuples
pub async fn dispatch_chain(
&self,
dispatches: &[(&ComputePipeline, &wgpu::BindGroup, [u32; 3])],
) -> GpuResult<()> {
self.dispatch_chain_with_config(dispatches, DispatchConfig::default())
.await
}
/// Dispatch chain with custom configuration.
pub async fn dispatch_chain_with_config(
&self,
dispatches: &[(&ComputePipeline, &wgpu::BindGroup, [u32; 3])],
config: DispatchConfig,
) -> GpuResult<()> {
if dispatches.is_empty() {
return Ok(());
}
let label = config.label.as_deref().unwrap_or("dispatch_chain");
debug!("Dispatching chain '{}' with {} kernels", label, dispatches.len());
let mut encoder = self
.device
.device()
.create_command_encoder(&wgpu::CommandEncoderDescriptor {
label: Some(label),
});
for (i, (pipeline, bind_group, workgroups)) in dispatches.iter().enumerate() {
trace!(
"Chain dispatch {}: workgroups [{}, {}, {}]",
i,
workgroups[0],
workgroups[1],
workgroups[2]
);
let pass_label = format!("{}_pass_{}", label, i);
let mut pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
label: Some(&pass_label),
timestamp_writes: None,
});
pass.set_pipeline(pipeline.pipeline());
pass.set_bind_group(0, Some(*bind_group), &[]);
pass.dispatch_workgroups(workgroups[0], workgroups[1], workgroups[2]);
}
self.device.submit(encoder.finish());
if config.wait {
self.device.poll(true);
}
Ok(())
}
/// Record dispatches to a command encoder without submitting.
///
/// This is useful when you want to combine compute with other operations.
pub fn record_dispatch(
&self,
encoder: &mut CommandEncoder,
pipeline: &ComputePipeline,
bind_group: &wgpu::BindGroup,
workgroups: [u32; 3],
label: Option<&str>,
) {
let pass_label = label.unwrap_or("recorded_dispatch");
let mut pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
label: Some(pass_label),
timestamp_writes: None,
});
pass.set_pipeline(pipeline.pipeline());
pass.set_bind_group(0, Some(bind_group), &[]);
pass.dispatch_workgroups(workgroups[0], workgroups[1], workgroups[2]);
}
/// Wait for all pending GPU work to complete.
pub fn synchronize(&self) {
self.device.poll(true);
}
/// Poll for completed work without blocking.
pub fn poll(&self) -> bool {
self.device.poll(false)
}
}
/// Builder for constructing complex dispatch operations
pub struct DispatchBuilder<'a> {
dispatcher: &'a GpuDispatcher,
dispatches: Vec<(Arc<ComputePipeline>, wgpu::BindGroup, [u32; 3])>,
config: DispatchConfig,
}
impl<'a> DispatchBuilder<'a> {
/// Create a new dispatch builder
pub fn new(dispatcher: &'a GpuDispatcher) -> Self {
Self {
dispatcher,
dispatches: Vec::new(),
config: DispatchConfig::default(),
}
}
/// Add a dispatch to the chain
pub fn add(
mut self,
pipeline: Arc<ComputePipeline>,
bind_group: wgpu::BindGroup,
workgroups: [u32; 3],
) -> Self {
self.dispatches.push((pipeline, bind_group, workgroups));
self
}
/// Set the configuration
pub fn config(mut self, config: DispatchConfig) -> Self {
self.config = config;
self
}
/// Set the label
pub fn label(mut self, label: impl Into<String>) -> Self {
self.config.label = Some(label.into());
self
}
/// Set wait flag
pub fn wait(mut self) -> Self {
self.config.wait = true;
self
}
/// Execute all dispatches
pub async fn execute(self) -> GpuResult<()> {
if self.dispatches.is_empty() {
return Ok(());
}
let refs: Vec<(&ComputePipeline, &wgpu::BindGroup, [u32; 3])> = self
.dispatches
.iter()
.map(|(p, b, w)| (p.as_ref(), b, *w))
.collect();
self.dispatcher
.dispatch_chain_with_config(&refs, self.config)
.await
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_dispatch_config_default() {
let config = DispatchConfig::default();
assert!(!config.wait);
assert!(config.label.is_none());
assert_eq!(config.timeout_ms, 0);
}
#[test]
fn test_dispatch_config_wait() {
let config = DispatchConfig::wait();
assert!(config.wait);
}
#[test]
fn test_dispatch_config_builder() {
let config = DispatchConfig::with_label("test")
.with_timeout(1000)
.with_wait(true);
assert_eq!(config.label.as_deref(), Some("test"));
assert_eq!(config.timeout_ms, 1000);
assert!(config.wait);
}
}

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//! GPU Coherence Engine
//!
//! Main entry point for GPU-accelerated coherence computation.
//! Provides automatic CPU fallback when GPU is unavailable.
use super::buffer::{BufferUsage, GpuBuffer, GpuBufferManager, GpuEdge, GpuParams, GpuRestrictionMap};
use super::error::{GpuError, GpuResult};
use super::kernels::{
AttentionWeight, ComputeEnergyKernel, ComputeResidualsKernel, EnergyParams, LaneStats,
RoutingDecision, SheafAttentionKernel, Token, TokenRoutingKernel,
};
use crate::coherence::{CoherenceEnergy as CpuCoherenceEnergy, EdgeEnergy, EnergyStatistics};
use crate::substrate::restriction::MatrixStorage;
use crate::substrate::{SheafGraph, NodeId, EdgeId};
use bytemuck::{Pod, Zeroable};
use chrono::Utc;
use std::collections::HashMap;
use std::sync::Arc;
use tracing::{debug, info, warn};
use wgpu::{
Adapter, Device, DeviceDescriptor, Features, Instance, InstanceDescriptor, Limits,
PowerPreference, Queue, RequestAdapterOptions,
};
/// GPU configuration
#[derive(Debug, Clone)]
pub struct GpuConfig {
/// Preferred power preference (high performance vs low power)
pub power_preference: PowerPreference,
/// Enable CPU fallback when GPU is unavailable
pub enable_fallback: bool,
/// Maximum buffer size in bytes (0 = no limit)
pub max_buffer_size: usize,
/// Beta parameter for attention computation
pub beta: f32,
/// Lane 0 (reflex) threshold
pub threshold_lane0: f32,
/// Lane 1 (retrieval) threshold
pub threshold_lane1: f32,
/// Lane 2 (heavy) threshold
pub threshold_lane2: f32,
/// Timeout for GPU operations in milliseconds
pub timeout_ms: u64,
}
impl Default for GpuConfig {
fn default() -> Self {
Self {
power_preference: PowerPreference::HighPerformance,
enable_fallback: true,
max_buffer_size: 0, // No limit
beta: 1.0,
threshold_lane0: 0.01,
threshold_lane1: 0.1,
threshold_lane2: 1.0,
timeout_ms: 5000,
}
}
}
/// GPU capabilities and limits
#[derive(Debug, Clone)]
pub struct GpuCapabilities {
/// Device name
pub device_name: String,
/// Vendor
pub vendor: String,
/// Backend (Vulkan, Metal, DX12, etc.)
pub backend: String,
/// Maximum buffer size
pub max_buffer_size: u64,
/// Maximum compute workgroup size
pub max_workgroup_size: u32,
/// Maximum compute workgroups per dimension
pub max_workgroups: [u32; 3],
/// Whether the GPU supports required features
pub supported: bool,
}
/// GPU energy result
#[derive(Debug, Clone)]
pub struct GpuCoherenceEnergy {
/// Total system energy
pub total_energy: f32,
/// Per-edge energies
pub edge_energies: Vec<f32>,
/// Edge indices (matches edge_energies)
pub edge_indices: Vec<EdgeId>,
/// Computation time in microseconds
pub compute_time_us: u64,
/// Whether GPU was used (false = CPU fallback)
pub used_gpu: bool,
}
impl GpuCoherenceEnergy {
/// Convert to CPU CoherenceEnergy format
pub fn to_cpu_format(&self, graph: &SheafGraph) -> CpuCoherenceEnergy {
let mut edge_energy_map = HashMap::new();
for (i, &edge_id) in self.edge_indices.iter().enumerate() {
let energy = self.edge_energies[i];
if let Some(edge) = graph.get_edge(edge_id) {
let edge_energy = EdgeEnergy::new_lightweight(
edge_id.to_string(),
edge.source.to_string(),
edge.target.to_string(),
energy / edge.weight.max(0.001), // Remove weight to get raw norm_sq
edge.weight,
);
edge_energy_map.insert(edge_id.to_string(), edge_energy);
}
}
CpuCoherenceEnergy::new(
edge_energy_map,
&HashMap::new(),
graph.node_count(),
format!("gpu-{}", Utc::now().timestamp()),
)
}
}
/// GPU-accelerated coherence engine
pub struct GpuCoherenceEngine {
device: Arc<Device>,
queue: Arc<Queue>,
buffer_manager: GpuBufferManager,
config: GpuConfig,
capabilities: GpuCapabilities,
// Kernels
residuals_kernel: ComputeResidualsKernel,
energy_kernel: ComputeEnergyKernel,
attention_kernel: SheafAttentionKernel,
routing_kernel: TokenRoutingKernel,
// Cached graph data
graph_data: Option<GpuGraphData>,
}
/// Cached graph data on GPU
struct GpuGraphData {
num_nodes: u32,
num_edges: u32,
state_dim: u32,
node_id_map: HashMap<NodeId, u32>,
edge_id_map: HashMap<EdgeId, u32>,
edge_id_reverse: Vec<EdgeId>,
}
impl GpuCoherenceEngine {
/// Create a new GPU coherence engine
pub async fn new(config: GpuConfig) -> GpuResult<Self> {
// Create wgpu instance
let instance = Instance::new(InstanceDescriptor::default());
// Request adapter
let adapter = instance
.request_adapter(&RequestAdapterOptions {
power_preference: config.power_preference,
compatible_surface: None,
force_fallback_adapter: false,
})
.await
.ok_or_else(|| GpuError::AdapterRequest("No suitable GPU adapter found".into()))?;
let capabilities = Self::get_capabilities(&adapter);
if !capabilities.supported {
return Err(GpuError::UnsupportedFeature(
"GPU does not support required features".into(),
));
}
info!(
"Using GPU: {} ({}) - {}",
capabilities.device_name, capabilities.vendor, capabilities.backend
);
// Request device
let (device, queue) = adapter
.request_device(
&DeviceDescriptor {
label: Some("prime_radiant_gpu"),
required_features: Features::empty(),
required_limits: Limits::default(),
memory_hints: Default::default(),
},
None,
)
.await
.map_err(|e| GpuError::DeviceCreation(e.to_string()))?;
let device = Arc::new(device);
let queue = Arc::new(queue);
// Create kernels
let residuals_kernel = ComputeResidualsKernel::new(&device)?;
let energy_kernel = ComputeEnergyKernel::new(&device)?;
let attention_kernel = SheafAttentionKernel::new(&device)?;
let routing_kernel = TokenRoutingKernel::new(&device)?;
// Create buffer manager
let buffer_manager = GpuBufferManager::new(device.clone(), queue.clone());
Ok(Self {
device,
queue,
buffer_manager,
config,
capabilities,
residuals_kernel,
energy_kernel,
attention_kernel,
routing_kernel,
graph_data: None,
})
}
/// Try to create a GPU engine, returning None if GPU is unavailable
pub async fn try_new(config: GpuConfig) -> Option<Self> {
match Self::new(config).await {
Ok(engine) => Some(engine),
Err(e) => {
warn!("GPU initialization failed: {}. Will use CPU fallback.", e);
None
}
}
}
/// Get GPU capabilities
fn get_capabilities(adapter: &Adapter) -> GpuCapabilities {
let info = adapter.get_info();
let limits = adapter.limits();
GpuCapabilities {
device_name: info.name,
vendor: format!("{:?}", info.vendor),
backend: format!("{:?}", info.backend),
max_buffer_size: limits.max_buffer_size as u64,
max_workgroup_size: limits.max_compute_workgroup_size_x,
max_workgroups: [
limits.max_compute_workgroups_per_dimension,
limits.max_compute_workgroups_per_dimension,
limits.max_compute_workgroups_per_dimension,
],
supported: true,
}
}
/// Upload graph data to GPU
pub fn upload_graph(&mut self, graph: &SheafGraph) -> GpuResult<()> {
if graph.edge_count() == 0 {
return Err(GpuError::EmptyGraph);
}
let num_nodes = graph.node_count() as u32;
let num_edges = graph.edge_count() as u32;
// Build node ID mapping
let mut node_id_map = HashMap::new();
let node_ids = graph.node_ids();
for (i, node_id) in node_ids.iter().enumerate() {
node_id_map.insert(*node_id, i as u32);
}
// Determine state dimension from first node
let state_dim = node_ids
.first()
.and_then(|id| graph.get_node(*id))
.map(|n| n.dim())
.unwrap_or(64) as u32;
// Flatten node states
let mut node_states: Vec<f32> = Vec::with_capacity((num_nodes * state_dim) as usize);
for node_id in &node_ids {
if let Some(state) = graph.node_state(*node_id) {
node_states.extend(state.iter().cloned());
// Pad if needed
for _ in state.len()..(state_dim as usize) {
node_states.push(0.0);
}
}
}
// Build edge data and restriction maps
let mut edges: Vec<GpuEdge> = Vec::with_capacity(num_edges as usize);
let mut restriction_maps: Vec<GpuRestrictionMap> = Vec::new();
let mut restriction_data: Vec<f32> = Vec::new();
let mut edge_id_map = HashMap::new();
let mut edge_id_reverse = Vec::new();
let edge_ids = graph.edge_ids();
for (i, edge_id) in edge_ids.iter().enumerate() {
edge_id_map.insert(*edge_id, i as u32);
edge_id_reverse.push(*edge_id);
if let Some(edge) = graph.get_edge(*edge_id) {
let source_idx = *node_id_map.get(&edge.source).unwrap_or(&0);
let target_idx = *node_id_map.get(&edge.target).unwrap_or(&0);
// Convert restriction maps
let rho_source_idx = restriction_maps.len() as u32;
let gpu_rho_source = Self::convert_restriction_map(
&edge.rho_source,
&mut restriction_data,
);
restriction_maps.push(gpu_rho_source);
let rho_target_idx = restriction_maps.len() as u32;
let gpu_rho_target = Self::convert_restriction_map(
&edge.rho_target,
&mut restriction_data,
);
restriction_maps.push(gpu_rho_target);
edges.push(GpuEdge {
source_idx,
target_idx,
weight: edge.weight,
rho_source_idx,
rho_target_idx,
comparison_dim: edge.comparison_dim() as u32,
_padding: [0; 2],
});
}
}
// Ensure restriction_data is not empty (GPU buffers can't be zero-sized)
if restriction_data.is_empty() {
restriction_data.push(0.0);
}
// Upload to GPU
self.buffer_manager.allocate_with_data(
&node_states,
BufferUsage::NodeStates,
"node_states",
)?;
self.buffer_manager.allocate_with_data(
&edges,
BufferUsage::EdgeData,
"edges",
)?;
self.buffer_manager.allocate_with_data(
&restriction_maps,
BufferUsage::RestrictionMaps,
"restriction_maps",
)?;
self.buffer_manager.allocate_with_data(
&restriction_data,
BufferUsage::RestrictionMaps,
"restriction_data",
)?;
// Allocate output buffers
let max_comparison_dim = edges.iter().map(|e| e.comparison_dim).max().unwrap_or(state_dim);
let residuals_size = (num_edges * max_comparison_dim) as usize * std::mem::size_of::<f32>();
let energies_size = num_edges as usize * std::mem::size_of::<f32>();
self.buffer_manager.allocate(
residuals_size,
BufferUsage::Residuals,
"residuals",
)?;
self.buffer_manager.allocate(
energies_size,
BufferUsage::Energies,
"edge_energies",
)?;
// Store graph data
self.graph_data = Some(GpuGraphData {
num_nodes,
num_edges,
state_dim,
node_id_map,
edge_id_map,
edge_id_reverse,
});
debug!(
"Uploaded graph to GPU: {} nodes, {} edges, state_dim={}",
num_nodes, num_edges, state_dim
);
Ok(())
}
/// Convert a RestrictionMap to GPU format
fn convert_restriction_map(
map: &crate::substrate::RestrictionMap,
data: &mut Vec<f32>,
) -> GpuRestrictionMap {
let data_offset = data.len() as u32;
let (map_type, data_len) = match &map.matrix {
MatrixStorage::Identity => (0, 0),
MatrixStorage::Diagonal(scales) => {
data.extend(scales.iter().cloned());
(1, scales.len() as u32)
}
MatrixStorage::Projection { indices, .. } => {
data.extend(indices.iter().map(|&i| i as f32));
(2, indices.len() as u32)
}
MatrixStorage::Sparse { values, .. } => {
// Simplified: just store values (would need row/col in practice)
data.extend(values.iter().cloned());
(3, values.len() as u32)
}
MatrixStorage::Dense { data: matrix_data, .. } => {
data.extend(matrix_data.iter().cloned());
(3, matrix_data.len() as u32)
}
};
GpuRestrictionMap {
map_type,
input_dim: map.input_dim() as u32,
output_dim: map.output_dim() as u32,
data_offset,
data_len,
_padding: [0; 3],
}
}
/// Compute coherence energy on GPU
pub async fn compute_energy(&mut self) -> GpuResult<GpuCoherenceEnergy> {
let start = std::time::Instant::now();
let graph_data = self.graph_data.as_ref()
.ok_or_else(|| GpuError::Internal("Graph not uploaded".into()))?;
let num_edges = graph_data.num_edges;
let state_dim = graph_data.state_dim;
// Create params buffer
let params = GpuParams {
num_edges,
num_nodes: graph_data.num_nodes,
state_dim,
beta: self.config.beta,
threshold_lane0: self.config.threshold_lane0,
threshold_lane1: self.config.threshold_lane1,
threshold_lane2: self.config.threshold_lane2,
_padding: 0,
};
self.buffer_manager.allocate_with_data(
&[params],
BufferUsage::Uniforms,
"params",
)?;
// Get buffers and create bind group for residuals kernel
// Note: We scope the borrows to avoid borrow checker issues with later allocations
let residuals_bind_group = {
let params_buf = self.buffer_manager.get("params")
.ok_or_else(|| GpuError::Internal("Params buffer not found".into()))?;
let node_states_buf = self.buffer_manager.get("node_states")
.ok_or_else(|| GpuError::Internal("Node states buffer not found".into()))?;
let edges_buf = self.buffer_manager.get("edges")
.ok_or_else(|| GpuError::Internal("Edges buffer not found".into()))?;
let restriction_maps_buf = self.buffer_manager.get("restriction_maps")
.ok_or_else(|| GpuError::Internal("Restriction maps buffer not found".into()))?;
let restriction_data_buf = self.buffer_manager.get("restriction_data")
.ok_or_else(|| GpuError::Internal("Restriction data buffer not found".into()))?;
let residuals_buf = self.buffer_manager.get("residuals")
.ok_or_else(|| GpuError::Internal("Residuals buffer not found".into()))?;
let energies_buf = self.buffer_manager.get("edge_energies")
.ok_or_else(|| GpuError::Internal("Edge energies buffer not found".into()))?;
self.residuals_kernel.create_bind_group(
&self.device,
params_buf,
node_states_buf,
edges_buf,
restriction_maps_buf,
restriction_data_buf,
residuals_buf,
energies_buf,
)
};
// Create command encoder
let mut encoder = self.device.create_command_encoder(&wgpu::CommandEncoderDescriptor {
label: Some("compute_energy_encoder"),
});
// Dispatch residuals computation
{
let mut compute_pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
label: Some("compute_residuals_pass"),
timestamp_writes: None,
});
compute_pass.set_pipeline(self.residuals_kernel.pipeline());
compute_pass.set_bind_group(0, &residuals_bind_group, &[]);
compute_pass.dispatch_workgroups(
ComputeResidualsKernel::workgroup_count(num_edges),
1,
1,
);
}
// Now reduce to get total energy
let energy_params = EnergyParams {
num_elements: num_edges,
_padding: [0; 7],
};
// Allocate energy computation buffers
let num_workgroups = ComputeEnergyKernel::workgroup_count(num_edges);
self.buffer_manager.allocate_with_data(
&[energy_params],
BufferUsage::Uniforms,
"energy_params",
)?;
self.buffer_manager.allocate(
(num_workgroups as usize).max(1) * std::mem::size_of::<f32>(),
BufferUsage::Energies,
"partial_sums",
)?;
// Create energy bind group in a scoped borrow
let energy_bind_group = {
let energy_params_buf = self.buffer_manager.get("energy_params").unwrap();
let energies_buf = self.buffer_manager.get("edge_energies").unwrap();
let partial_sums_buf = self.buffer_manager.get("partial_sums").unwrap();
self.energy_kernel.create_bind_group(
&self.device,
energy_params_buf,
energies_buf,
partial_sums_buf,
)
};
// Dispatch energy reduction
{
let mut compute_pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
label: Some("compute_energy_pass"),
timestamp_writes: None,
});
compute_pass.set_pipeline(self.energy_kernel.main_pipeline());
compute_pass.set_bind_group(0, &energy_bind_group, &[]);
compute_pass.dispatch_workgroups(num_workgroups, 1, 1);
}
// If we have multiple workgroups, do final reduction
if num_workgroups > 1 {
let final_params = EnergyParams {
num_elements: num_workgroups,
_padding: [0; 7],
};
self.buffer_manager.allocate_with_data(
&[final_params],
BufferUsage::Uniforms,
"final_params",
)?;
self.buffer_manager.allocate(
std::mem::size_of::<f32>(),
BufferUsage::Energies,
"total_energy",
)?;
// Create final bind group in a scoped borrow
let final_bind_group = {
let final_params_buf = self.buffer_manager.get("final_params").unwrap();
let partial_sums_buf = self.buffer_manager.get("partial_sums").unwrap();
let total_energy_buf = self.buffer_manager.get("total_energy").unwrap();
self.energy_kernel.create_bind_group(
&self.device,
final_params_buf,
partial_sums_buf,
total_energy_buf,
)
};
{
let mut compute_pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
label: Some("final_reduce_pass"),
timestamp_writes: None,
});
compute_pass.set_pipeline(self.energy_kernel.final_pipeline());
compute_pass.set_bind_group(0, &final_bind_group, &[]);
compute_pass.dispatch_workgroups(1, 1, 1);
}
}
// Create staging buffers for readback
let energies_staging = self.device.create_buffer(&wgpu::BufferDescriptor {
label: Some("energies_staging"),
size: (num_edges as usize * std::mem::size_of::<f32>()) as u64,
usage: wgpu::BufferUsages::MAP_READ | wgpu::BufferUsages::COPY_DST,
mapped_at_creation: false,
});
let total_staging = self.device.create_buffer(&wgpu::BufferDescriptor {
label: Some("total_staging"),
size: std::mem::size_of::<f32>() as u64,
usage: wgpu::BufferUsages::MAP_READ | wgpu::BufferUsages::COPY_DST,
mapped_at_creation: false,
});
// Copy results to staging - get buffer references in scoped borrow
{
let energies_buf = self.buffer_manager.get("edge_energies").unwrap();
encoder.copy_buffer_to_buffer(
&energies_buf.buffer,
0,
&energies_staging,
0,
(num_edges as usize * std::mem::size_of::<f32>()) as u64,
);
}
if num_workgroups > 1 {
let total_buf = self.buffer_manager.get("total_energy").unwrap();
encoder.copy_buffer_to_buffer(
&total_buf.buffer,
0,
&total_staging,
0,
std::mem::size_of::<f32>() as u64,
);
} else {
let partial_sums_buf = self.buffer_manager.get("partial_sums").unwrap();
encoder.copy_buffer_to_buffer(
&partial_sums_buf.buffer,
0,
&total_staging,
0,
std::mem::size_of::<f32>() as u64,
);
}
// Submit commands
self.queue.submit(std::iter::once(encoder.finish()));
// Read back results
let edge_energies = Self::read_buffer_f32(&self.device, &energies_staging, num_edges as usize).await?;
let total_energy = Self::read_buffer_f32(&self.device, &total_staging, 1).await?[0];
let compute_time_us = start.elapsed().as_micros() as u64;
debug!(
"GPU energy computation: total={:.6}, {} edges, {}us",
total_energy, num_edges, compute_time_us
);
Ok(GpuCoherenceEnergy {
total_energy,
edge_energies,
edge_indices: graph_data.edge_id_reverse.clone(),
compute_time_us,
used_gpu: true,
})
}
/// Read f32 buffer back to CPU
async fn read_buffer_f32(
device: &Device,
buffer: &wgpu::Buffer,
count: usize,
) -> GpuResult<Vec<f32>> {
let buffer_slice = buffer.slice(..);
let (sender, receiver) = futures::channel::oneshot::channel();
buffer_slice.map_async(wgpu::MapMode::Read, move |result| {
let _ = sender.send(result);
});
device.poll(wgpu::Maintain::Wait);
receiver
.await
.map_err(|_| GpuError::BufferRead("Channel closed".into()))?
.map_err(|e| GpuError::BufferRead(e.to_string()))?;
let data = buffer_slice.get_mapped_range();
let result: Vec<f32> = bytemuck::cast_slice(&data[..count * std::mem::size_of::<f32>()])
.to_vec();
drop(data);
buffer.unmap();
Ok(result)
}
/// Get GPU capabilities
pub fn capabilities(&self) -> &GpuCapabilities {
&self.capabilities
}
/// Get configuration
pub fn config(&self) -> &GpuConfig {
&self.config
}
/// Check if GPU is available
pub fn is_available(&self) -> bool {
self.capabilities.supported
}
/// Release all GPU resources
pub fn release(&mut self) {
self.buffer_manager.clear();
self.graph_data = None;
}
}
/// Synchronous wrapper for GPU coherence engine using pollster
pub mod sync {
use super::*;
/// Synchronously create a GPU engine
pub fn create_engine(config: GpuConfig) -> GpuResult<GpuCoherenceEngine> {
pollster::block_on(GpuCoherenceEngine::new(config))
}
/// Try to create GPU engine synchronously
pub fn try_create_engine(config: GpuConfig) -> Option<GpuCoherenceEngine> {
pollster::block_on(GpuCoherenceEngine::try_new(config))
}
/// Compute energy synchronously
pub fn compute_energy(engine: &mut GpuCoherenceEngine) -> GpuResult<GpuCoherenceEnergy> {
pollster::block_on(engine.compute_energy())
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_gpu_config_default() {
let config = GpuConfig::default();
assert!(config.enable_fallback);
assert_eq!(config.beta, 1.0);
assert!(config.threshold_lane0 < config.threshold_lane1);
assert!(config.threshold_lane1 < config.threshold_lane2);
}
#[test]
fn test_gpu_params_size() {
assert_eq!(std::mem::size_of::<GpuParams>(), 32);
}
#[test]
fn test_energy_params_size() {
assert_eq!(std::mem::size_of::<EnergyParams>(), 32);
}
}

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//! GPU Error Types
//!
//! Error handling for GPU operations including device initialization,
//! buffer management, shader execution, and kernel dispatch.
use thiserror::Error;
/// Result type for GPU operations
pub type GpuResult<T> = Result<T, GpuError>;
/// Errors that can occur during GPU operations
#[derive(Debug, Error)]
pub enum GpuError {
/// No suitable GPU adapter found
#[error("No suitable GPU adapter found. Ensure a GPU with compute capabilities is available.")]
NoAdapter,
/// No compatible GPU device found
#[error("No compatible GPU device found: {0}")]
NoDevice(String),
/// GPU device creation failed
#[error("Failed to create GPU device: {0}")]
DeviceCreation(String),
/// Device request failed
#[error("Failed to request GPU device: {0}")]
DeviceRequestFailed(String),
/// Shader compilation failed
#[error("Shader compilation failed: {0}")]
ShaderCompilation(String),
/// Buffer allocation failed
#[error("Buffer allocation failed: {0}")]
BufferAllocation(String),
/// Buffer allocation failed with details
#[error("Buffer allocation failed: requested {requested_bytes} bytes, reason: {reason}")]
BufferAllocationFailed {
/// Number of bytes requested
requested_bytes: u64,
/// Reason for failure
reason: String,
},
/// Buffer size exceeds maximum allowed
#[error("Buffer size {size} exceeds maximum allowed {max}")]
BufferTooLarge {
/// Requested size
size: u64,
/// Maximum allowed size
max: u64,
},
/// Buffer size mismatch
#[error("Buffer size mismatch: expected {expected}, got {actual}")]
BufferSizeMismatch { expected: usize, actual: usize },
/// Buffer read-back failed
#[error("Buffer read-back failed: {0}")]
BufferReadFailed(String),
/// Buffer mapping failed
#[error("Buffer mapping failed: {0}")]
BufferMapFailed(String),
/// Dimension mismatch
#[error("Dimension mismatch: expected {expected}, got {actual}")]
DimensionMismatch { expected: usize, actual: usize },
/// Invalid binding configuration
#[error("Invalid binding configuration: expected {expected} bindings, got {actual}")]
InvalidBindingCount {
/// Expected number of bindings
expected: usize,
/// Actual number of bindings
actual: usize,
},
/// Invalid workgroup configuration
#[error("Invalid workgroup configuration: [{x}, {y}, {z}] exceeds device limits")]
InvalidWorkgroupSize {
/// X dimension
x: u32,
/// Y dimension
y: u32,
/// Z dimension
z: u32,
},
/// Compute pipeline creation failed
#[error("Failed to create compute pipeline: {0}")]
PipelineCreation(String),
/// Command encoding failed
#[error("Command encoding failed: {0}")]
CommandEncoding(String),
/// GPU execution failed
#[error("GPU execution failed: {0}")]
ExecutionFailed(String),
/// Buffer read failed
#[error("Failed to read buffer: {0}")]
BufferRead(String),
/// Buffer write failed
#[error("Failed to write buffer: {0}")]
BufferWrite(String),
/// Timeout waiting for GPU operation
#[error("GPU operation timed out after {0}ms")]
Timeout(u64),
/// Graph has no edges
#[error("Graph has no edges to compute")]
EmptyGraph,
/// Invalid configuration
#[error("Invalid GPU configuration: {0}")]
InvalidConfig(String),
/// Feature not supported
#[error("GPU feature not supported: {0}")]
UnsupportedFeature(String),
/// Adapter request failed
#[error("Failed to request GPU adapter: {0}")]
AdapterRequest(String),
/// Out of GPU memory
#[error("Out of GPU memory: requested {requested_bytes} bytes")]
OutOfMemory {
/// Number of bytes requested
requested_bytes: u64,
},
/// Device lost
#[error("GPU device lost: {0}")]
DeviceLost(String),
/// Internal error
#[error("Internal GPU error: {0}")]
Internal(String),
}
impl GpuError {
/// Check if this error indicates GPU is unavailable and fallback should be used
pub fn should_fallback(&self) -> bool {
matches!(
self,
GpuError::NoAdapter
| GpuError::NoDevice(_)
| GpuError::DeviceCreation(_)
| GpuError::DeviceRequestFailed(_)
| GpuError::AdapterRequest(_)
| GpuError::UnsupportedFeature(_)
)
}
/// Check if this error is recoverable
pub fn is_recoverable(&self) -> bool {
matches!(
self,
GpuError::Timeout(_)
| GpuError::BufferRead(_)
| GpuError::BufferReadFailed(_)
| GpuError::ExecutionFailed(_)
)
}
}
impl From<wgpu::RequestDeviceError> for GpuError {
fn from(e: wgpu::RequestDeviceError) -> Self {
Self::DeviceRequestFailed(e.to_string())
}
}
impl From<wgpu::BufferAsyncError> for GpuError {
fn from(e: wgpu::BufferAsyncError) -> Self {
Self::BufferMapFailed(e.to_string())
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_should_fallback() {
assert!(GpuError::NoAdapter.should_fallback());
assert!(GpuError::NoDevice("test".into()).should_fallback());
assert!(GpuError::DeviceCreation("test".into()).should_fallback());
assert!(!GpuError::Timeout(100).should_fallback());
assert!(!GpuError::EmptyGraph.should_fallback());
}
#[test]
fn test_is_recoverable() {
assert!(GpuError::Timeout(100).is_recoverable());
assert!(GpuError::BufferRead("test".into()).is_recoverable());
assert!(GpuError::BufferReadFailed("test".into()).is_recoverable());
assert!(!GpuError::NoDevice("test".into()).is_recoverable());
assert!(!GpuError::NoAdapter.is_recoverable());
}
#[test]
fn test_error_display() {
let err = GpuError::BufferAllocationFailed {
requested_bytes: 1024,
reason: "out of memory".to_string(),
};
assert!(err.to_string().contains("1024"));
assert!(err.to_string().contains("out of memory"));
}
#[test]
fn test_workgroup_error() {
let err = GpuError::InvalidWorkgroupSize {
x: 1000,
y: 1,
z: 1,
};
let msg = err.to_string();
assert!(msg.contains("1000"));
}
}

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//! GPU Kernel Wrappers
//!
//! Provides Rust wrappers around WGSL compute shaders for coherence computation.
//! Each kernel handles pipeline creation, bind group setup, and dispatch.
use super::buffer::{
BufferUsage, GpuBuffer, GpuBufferManager, GpuEdge, GpuParams, GpuRestrictionMap,
};
use super::error::{GpuError, GpuResult};
use super::shaders;
use super::workgroup;
use bytemuck::{Pod, Zeroable};
use std::sync::Arc;
use wgpu::{
BindGroup, BindGroupDescriptor, BindGroupEntry, BindGroupLayout, BindGroupLayoutDescriptor,
BindGroupLayoutEntry, BindingResource, BindingType, BufferBindingType, ComputePipeline,
ComputePipelineDescriptor, Device, PipelineLayoutDescriptor, Queue, ShaderModule,
ShaderModuleDescriptor, ShaderSource, ShaderStages,
};
/// Compute residuals kernel
/// Computes r_e = rho_source(x_source) - rho_target(x_target) for all edges
pub struct ComputeResidualsKernel {
pipeline: ComputePipeline,
bind_group_layout: BindGroupLayout,
}
impl ComputeResidualsKernel {
/// Create a new compute residuals kernel
pub fn new(device: &Device) -> GpuResult<Self> {
let shader = device.create_shader_module(ShaderModuleDescriptor {
label: Some("compute_residuals"),
source: ShaderSource::Wgsl(shaders::COMPUTE_RESIDUALS.into()),
});
let bind_group_layout = device.create_bind_group_layout(&BindGroupLayoutDescriptor {
label: Some("compute_residuals_bind_group_layout"),
entries: &[
// Params uniform
BindGroupLayoutEntry {
binding: 0,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Uniform,
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Node states
BindGroupLayoutEntry {
binding: 1,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Edges
BindGroupLayoutEntry {
binding: 2,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Restriction maps
BindGroupLayoutEntry {
binding: 3,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Restriction data
BindGroupLayoutEntry {
binding: 4,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Residuals output
BindGroupLayoutEntry {
binding: 5,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: false },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Residual norms output
BindGroupLayoutEntry {
binding: 6,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: false },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
],
});
let pipeline_layout = device.create_pipeline_layout(&PipelineLayoutDescriptor {
label: Some("compute_residuals_pipeline_layout"),
bind_group_layouts: &[&bind_group_layout],
push_constant_ranges: &[],
});
let pipeline = device.create_compute_pipeline(&ComputePipelineDescriptor {
label: Some("compute_residuals_pipeline"),
layout: Some(&pipeline_layout),
module: &shader,
entry_point: Some("main"),
compilation_options: Default::default(),
cache: None,
});
Ok(Self {
pipeline,
bind_group_layout,
})
}
/// Create a bind group for execution
pub fn create_bind_group(
&self,
device: &Device,
params_buffer: &GpuBuffer,
node_states_buffer: &GpuBuffer,
edges_buffer: &GpuBuffer,
restriction_maps_buffer: &GpuBuffer,
restriction_data_buffer: &GpuBuffer,
residuals_buffer: &GpuBuffer,
residual_norms_buffer: &GpuBuffer,
) -> BindGroup {
device.create_bind_group(&BindGroupDescriptor {
label: Some("compute_residuals_bind_group"),
layout: &self.bind_group_layout,
entries: &[
BindGroupEntry {
binding: 0,
resource: params_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 1,
resource: node_states_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 2,
resource: edges_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 3,
resource: restriction_maps_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 4,
resource: restriction_data_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 5,
resource: residuals_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 6,
resource: residual_norms_buffer.buffer.as_entire_binding(),
},
],
})
}
/// Get the pipeline for use in command encoder
pub fn pipeline(&self) -> &ComputePipeline {
&self.pipeline
}
/// Calculate number of workgroups needed
pub fn workgroup_count(num_edges: u32) -> u32 {
// One thread per edge, 256 threads per workgroup
(num_edges + workgroup::SIZE_1D - 1) / workgroup::SIZE_1D
}
}
/// Compute energy kernel with parallel reduction
pub struct ComputeEnergyKernel {
main_pipeline: ComputePipeline,
final_pipeline: ComputePipeline,
bind_group_layout: BindGroupLayout,
}
/// Parameters for energy reduction
#[repr(C)]
#[derive(Debug, Clone, Copy, Pod, Zeroable)]
pub struct EnergyParams {
/// Number of elements to reduce
pub num_elements: u32,
/// Padding
pub _padding: [u32; 7],
}
impl ComputeEnergyKernel {
/// Create a new compute energy kernel
pub fn new(device: &Device) -> GpuResult<Self> {
let shader = device.create_shader_module(ShaderModuleDescriptor {
label: Some("compute_energy"),
source: ShaderSource::Wgsl(shaders::COMPUTE_ENERGY.into()),
});
let bind_group_layout = device.create_bind_group_layout(&BindGroupLayoutDescriptor {
label: Some("compute_energy_bind_group_layout"),
entries: &[
// Params uniform
BindGroupLayoutEntry {
binding: 0,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Uniform,
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Input energies
BindGroupLayoutEntry {
binding: 1,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Output partial sums
BindGroupLayoutEntry {
binding: 2,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: false },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
],
});
let pipeline_layout = device.create_pipeline_layout(&PipelineLayoutDescriptor {
label: Some("compute_energy_pipeline_layout"),
bind_group_layouts: &[&bind_group_layout],
push_constant_ranges: &[],
});
let main_pipeline = device.create_compute_pipeline(&ComputePipelineDescriptor {
label: Some("compute_energy_main_pipeline"),
layout: Some(&pipeline_layout),
module: &shader,
entry_point: Some("main"),
compilation_options: Default::default(),
cache: None,
});
let final_pipeline = device.create_compute_pipeline(&ComputePipelineDescriptor {
label: Some("compute_energy_final_pipeline"),
layout: Some(&pipeline_layout),
module: &shader,
entry_point: Some("final_reduce"),
compilation_options: Default::default(),
cache: None,
});
Ok(Self {
main_pipeline,
final_pipeline,
bind_group_layout,
})
}
/// Create a bind group for execution
pub fn create_bind_group(
&self,
device: &Device,
params_buffer: &GpuBuffer,
input_buffer: &GpuBuffer,
output_buffer: &GpuBuffer,
) -> BindGroup {
device.create_bind_group(&BindGroupDescriptor {
label: Some("compute_energy_bind_group"),
layout: &self.bind_group_layout,
entries: &[
BindGroupEntry {
binding: 0,
resource: params_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 1,
resource: input_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 2,
resource: output_buffer.buffer.as_entire_binding(),
},
],
})
}
/// Get the main reduction pipeline
pub fn main_pipeline(&self) -> &ComputePipeline {
&self.main_pipeline
}
/// Get the final reduction pipeline
pub fn final_pipeline(&self) -> &ComputePipeline {
&self.final_pipeline
}
/// Calculate number of workgroups for first pass
pub fn workgroup_count(num_elements: u32) -> u32 {
// One element per thread, 256 threads per workgroup
(num_elements + workgroup::SIZE_1D - 1) / workgroup::SIZE_1D
}
}
/// Sheaf attention kernel
pub struct SheafAttentionKernel {
single_pass_pipeline: ComputePipeline,
bind_group_layout: BindGroupLayout,
}
/// Attention weight output
#[repr(C)]
#[derive(Debug, Clone, Copy, Pod, Zeroable)]
pub struct AttentionWeight {
pub edge_idx: u32,
pub source_idx: u32,
pub target_idx: u32,
pub raw_score: f32,
pub attention: f32,
pub _padding: [u32; 3],
}
impl SheafAttentionKernel {
/// Create a new sheaf attention kernel
pub fn new(device: &Device) -> GpuResult<Self> {
let shader = device.create_shader_module(ShaderModuleDescriptor {
label: Some("sheaf_attention"),
source: ShaderSource::Wgsl(shaders::SHEAF_ATTENTION.into()),
});
let bind_group_layout = device.create_bind_group_layout(&BindGroupLayoutDescriptor {
label: Some("sheaf_attention_bind_group_layout"),
entries: &[
// Params
BindGroupLayoutEntry {
binding: 0,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Uniform,
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Edges
BindGroupLayoutEntry {
binding: 1,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Edge energies
BindGroupLayoutEntry {
binding: 2,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Attention weights output
BindGroupLayoutEntry {
binding: 3,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: false },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Node exp sums (for normalization)
BindGroupLayoutEntry {
binding: 4,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: false },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
],
});
let pipeline_layout = device.create_pipeline_layout(&PipelineLayoutDescriptor {
label: Some("sheaf_attention_pipeline_layout"),
bind_group_layouts: &[&bind_group_layout],
push_constant_ranges: &[],
});
let single_pass_pipeline = device.create_compute_pipeline(&ComputePipelineDescriptor {
label: Some("sheaf_attention_single_pass_pipeline"),
layout: Some(&pipeline_layout),
module: &shader,
entry_point: Some("compute_attention_single_pass"),
compilation_options: Default::default(),
cache: None,
});
Ok(Self {
single_pass_pipeline,
bind_group_layout,
})
}
/// Create a bind group
pub fn create_bind_group(
&self,
device: &Device,
params_buffer: &GpuBuffer,
edges_buffer: &GpuBuffer,
edge_energies_buffer: &GpuBuffer,
attention_weights_buffer: &GpuBuffer,
node_exp_sums_buffer: &GpuBuffer,
) -> BindGroup {
device.create_bind_group(&BindGroupDescriptor {
label: Some("sheaf_attention_bind_group"),
layout: &self.bind_group_layout,
entries: &[
BindGroupEntry {
binding: 0,
resource: params_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 1,
resource: edges_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 2,
resource: edge_energies_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 3,
resource: attention_weights_buffer.buffer.as_entire_binding(),
},
BindGroupEntry {
binding: 4,
resource: node_exp_sums_buffer.buffer.as_entire_binding(),
},
],
})
}
/// Get the single-pass pipeline
pub fn pipeline(&self) -> &ComputePipeline {
&self.single_pass_pipeline
}
/// Calculate workgroup count
pub fn workgroup_count(num_edges: u32) -> u32 {
(num_edges + workgroup::SIZE_1D - 1) / workgroup::SIZE_1D
}
}
/// Token routing kernel
pub struct TokenRoutingKernel {
route_pipeline: ComputePipeline,
bind_group_layout: BindGroupLayout,
}
/// Token input
#[repr(C)]
#[derive(Debug, Clone, Copy, Pod, Zeroable)]
pub struct Token {
pub token_id: u32,
pub node_idx: u32,
pub action_type: u32,
pub priority: f32,
}
/// Routing decision output
#[repr(C)]
#[derive(Debug, Clone, Copy, Pod, Zeroable)]
pub struct RoutingDecision {
pub token_id: u32,
pub assigned_lane: u32,
pub local_energy: f32,
pub confidence: f32,
pub escalation_reason: u32,
pub num_high_energy_edges: u32,
pub max_edge_energy: f32,
pub _padding: u32,
}
/// Lane statistics
#[repr(C)]
#[derive(Debug, Clone, Copy, Pod, Zeroable)]
pub struct LaneStats {
pub lane_counts: [u32; 4],
pub total_energy_per_lane: [f32; 4],
pub _padding: [u32; 8],
}
impl TokenRoutingKernel {
/// Create a new token routing kernel
pub fn new(device: &Device) -> GpuResult<Self> {
let shader = device.create_shader_module(ShaderModuleDescriptor {
label: Some("token_routing"),
source: ShaderSource::Wgsl(shaders::TOKEN_ROUTING.into()),
});
let bind_group_layout = device.create_bind_group_layout(&BindGroupLayoutDescriptor {
label: Some("token_routing_bind_group_layout"),
entries: &[
// Params
BindGroupLayoutEntry {
binding: 0,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Uniform,
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Tokens
BindGroupLayoutEntry {
binding: 1,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Local energies
BindGroupLayoutEntry {
binding: 2,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Edge energies
BindGroupLayoutEntry {
binding: 3,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Node edge counts
BindGroupLayoutEntry {
binding: 4,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Node edge offsets
BindGroupLayoutEntry {
binding: 5,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Node edges
BindGroupLayoutEntry {
binding: 6,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Routing decisions output
BindGroupLayoutEntry {
binding: 7,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: false },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
// Lane stats output
BindGroupLayoutEntry {
binding: 8,
visibility: ShaderStages::COMPUTE,
ty: BindingType::Buffer {
ty: BufferBindingType::Storage { read_only: false },
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
],
});
let pipeline_layout = device.create_pipeline_layout(&PipelineLayoutDescriptor {
label: Some("token_routing_pipeline_layout"),
bind_group_layouts: &[&bind_group_layout],
push_constant_ranges: &[],
});
let route_pipeline = device.create_compute_pipeline(&ComputePipelineDescriptor {
label: Some("token_routing_pipeline"),
layout: Some(&pipeline_layout),
module: &shader,
entry_point: Some("route_tokens"),
compilation_options: Default::default(),
cache: None,
});
Ok(Self {
route_pipeline,
bind_group_layout,
})
}
/// Get the routing pipeline
pub fn pipeline(&self) -> &ComputePipeline {
&self.route_pipeline
}
/// Get bind group layout
pub fn bind_group_layout(&self) -> &BindGroupLayout {
&self.bind_group_layout
}
/// Calculate workgroup count
pub fn workgroup_count(num_tokens: u32) -> u32 {
(num_tokens + workgroup::SIZE_1D - 1) / workgroup::SIZE_1D
}
}

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//! GPU acceleration module for Prime-Radiant coherence engine.
//!
//! This module provides GPU-accelerated computation using wgpu for:
//! - Parallel residual calculations across large graphs
//! - Matrix operations for restriction maps
//! - Energy aggregation with atomic operations
//! - Spectral analysis via power iteration
//!
//! # Architecture
//!
//! ```text
//! +------------------+ +------------------+ +------------------+
//! | GpuDevice |---->| GpuBuffer |---->| GpuDispatcher |
//! | (Init/Queue) | | (Alloc/Transfer)| | (Kernels/Sync) |
//! +------------------+ +------------------+ +------------------+
//! | | |
//! v v v
//! +------------------+ +------------------+ +------------------+
//! | Instance/Adapter | | BufferPool | | PipelineCache |
//! | Device/Queue | | Read/Write | | BindGroups |
//! +------------------+ +------------------+ +------------------+
//! ```
//!
//! # Feature Flag
//!
//! This module requires the `gpu` feature flag:
//! ```toml
//! [dependencies]
//! prime-radiant = { version = "0.1", features = ["gpu"] }
//! ```
//!
//! # Example
//!
//! ```rust,ignore
//! use prime_radiant::gpu::{GpuDevice, GpuBuffer, GpuDispatcher, ComputePipeline};
//!
//! #[tokio::main]
//! async fn main() -> Result<(), Box<dyn std::error::Error>> {
//! // Initialize GPU device
//! let device = GpuDevice::new().await?;
//!
//! // Create storage buffer with data
//! let input_data: Vec<f32> = vec![1.0, 2.0, 3.0, 4.0];
//! let input_buffer = GpuBuffer::new_storage(device.device(), &input_data, false);
//!
//! // Create output buffer
//! let output_buffer = GpuBuffer::new_storage_uninit::<f32>(
//! device.device(),
//! input_data.len(),
//! true,
//! );
//!
//! // Create compute pipeline
//! let pipeline = ComputePipeline::from_shader(
//! device.device(),
//! include_str!("shaders/compute_residuals.wgsl"),
//! "main",
//! &[BindingDesc::storage_readonly(), BindingDesc::storage_readwrite()],
//! )?;
//!
//! // Create dispatcher and execute
//! let dispatcher = GpuDispatcher::new(Arc::new(device));
//! let bind_group = pipeline.create_bind_group(
//! dispatcher.device().device(),
//! &[&input_buffer, &output_buffer],
//! )?;
//! dispatcher.dispatch(&pipeline, &bind_group, [4, 1, 1]).await?;
//!
//! Ok(())
//! }
//! ```
//!
//! # GPU Kernels
//!
//! The following WGSL compute shaders are implemented:
//!
//! 1. **compute_residuals.wgsl** - Parallel residual computation for all edges
//! 2. **compute_energy.wgsl** - Parallel energy aggregation with tree reduction
//! 3. **sheaf_attention.wgsl** - Batched attention: A_ij = exp(-beta * E_ij) / Z
//! 4. **token_routing.wgsl** - Parallel lane assignment based on energy thresholds
//!
//! # Performance Targets
//!
//! | Operation | Target | Notes |
//! |-----------|--------|-------|
//! | Buffer allocation | < 1ms | Pooled for hot paths |
//! | Kernel dispatch | < 100us | Excludes GPU execution |
//! | Residual (10K edges) | < 1ms | GPU parallel |
//! | Energy aggregation | < 500us | Atomic reduction |
mod buffer;
mod device;
mod dispatch;
mod engine;
mod error;
mod kernels;
mod pipeline;
// Core exports
pub use buffer::{BufferUsage, GpuBuffer, GpuBufferManager, GpuBufferPool, BufferUsageFlags, BufferKey};
pub use device::{GpuDevice, GpuDeviceInfo, GpuDeviceOptions};
pub use dispatch::{DispatchConfig, GpuDispatcher, DispatchBuilder};
pub use error::{GpuError, GpuResult};
pub use pipeline::{BindingDesc, BindingType, ComputePipeline, PipelineCache};
// Re-export buffer types
pub use buffer::{GpuNodeState, GpuEdge, GpuRestrictionMap, GpuParams};
// Re-export engine types
pub use engine::{GpuCoherenceEngine, GpuConfig, GpuCapabilities, GpuCoherenceEnergy};
/// Synchronous API for GPU coherence engine (uses pollster)
pub mod sync {
pub use super::engine::sync::*;
}
// Re-export kernel types
pub use kernels::{
ComputeResidualsKernel, ComputeEnergyKernel, SheafAttentionKernel, TokenRoutingKernel,
AttentionWeight, Token, RoutingDecision, LaneStats, EnergyParams,
};
/// Default workgroup size for compute shaders
pub const DEFAULT_WORKGROUP_SIZE: u32 = 256;
/// Maximum buffer size for a single allocation (256MB)
pub const MAX_BUFFER_SIZE: u64 = 256 * 1024 * 1024;
/// Default pool capacity for buffer reuse
pub const DEFAULT_POOL_CAPACITY: usize = 32;
/// Shader source code embedded at compile time
pub mod shaders {
/// Compute residuals shader for parallel edge residual computation
pub const COMPUTE_RESIDUALS: &str = include_str!("shaders/compute_residuals.wgsl");
/// Compute energy shader for parallel reduction
pub const COMPUTE_ENERGY: &str = include_str!("shaders/compute_energy.wgsl");
/// Sheaf attention shader for attention weight computation
pub const SHEAF_ATTENTION: &str = include_str!("shaders/sheaf_attention.wgsl");
/// Token routing shader for lane assignment
pub const TOKEN_ROUTING: &str = include_str!("shaders/token_routing.wgsl");
}
/// GPU workgroup size constants
pub mod workgroup {
/// Default workgroup size for 1D compute
pub const SIZE_1D: u32 = 256;
/// Default workgroup size for 2D compute (x dimension)
pub const SIZE_2D_X: u32 = 16;
/// Default workgroup size for 2D compute (y dimension)
pub const SIZE_2D_Y: u32 = 16;
/// Maximum state vector dimension for GPU kernels
pub const MAX_STATE_DIM: u32 = 512;
}

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//! Compute pipeline management for GPU operations.
//!
//! This module handles shader compilation, pipeline creation, and bind group
//! management for GPU compute operations.
use std::sync::Arc;
use dashmap::DashMap;
use tracing::{debug, info};
use wgpu::{Device, ShaderModule};
use super::buffer::GpuBuffer;
use super::error::{GpuError, GpuResult};
use super::DEFAULT_WORKGROUP_SIZE;
/// Type of binding in a compute shader
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum BindingType {
/// Storage buffer (read-only)
StorageReadonly,
/// Storage buffer (read-write)
StorageReadWrite,
/// Uniform buffer
Uniform,
}
impl BindingType {
/// Convert to wgpu binding type
fn to_wgpu(&self) -> wgpu::BindingType {
match self {
Self::StorageReadonly => wgpu::BindingType::Buffer {
ty: wgpu::BufferBindingType::Storage { read_only: true },
has_dynamic_offset: false,
min_binding_size: None,
},
Self::StorageReadWrite => wgpu::BindingType::Buffer {
ty: wgpu::BufferBindingType::Storage { read_only: false },
has_dynamic_offset: false,
min_binding_size: None,
},
Self::Uniform => wgpu::BindingType::Buffer {
ty: wgpu::BufferBindingType::Uniform,
has_dynamic_offset: false,
min_binding_size: None,
},
}
}
}
/// Description of a binding in a compute shader
#[derive(Debug, Clone)]
pub struct BindingDesc {
/// Binding type
pub binding_type: BindingType,
/// Optional label for debugging
pub label: Option<String>,
}
impl BindingDesc {
/// Create a storage read-only binding
pub fn storage_readonly() -> Self {
Self {
binding_type: BindingType::StorageReadonly,
label: None,
}
}
/// Create a storage read-write binding
pub fn storage_readwrite() -> Self {
Self {
binding_type: BindingType::StorageReadWrite,
label: None,
}
}
/// Create a uniform binding
pub fn uniform() -> Self {
Self {
binding_type: BindingType::Uniform,
label: None,
}
}
/// Add a label to the binding
pub fn with_label(mut self, label: impl Into<String>) -> Self {
self.label = Some(label.into());
self
}
}
/// Compute pipeline wrapper
pub struct ComputePipeline {
pipeline: wgpu::ComputePipeline,
bind_group_layout: wgpu::BindGroupLayout,
workgroup_size: [u32; 3],
entry_point: String,
binding_count: usize,
}
impl ComputePipeline {
/// Create a new compute pipeline from shader source.
///
/// # Arguments
///
/// * `device` - The wgpu device
/// * `shader_source` - WGSL shader source code
/// * `entry_point` - Entry point function name
/// * `bindings` - Binding descriptions
///
/// # Example
///
/// ```rust,ignore
/// let pipeline = ComputePipeline::from_shader(
/// &device,
/// r#"
/// @group(0) @binding(0) var<storage, read> input: array<f32>;
/// @group(0) @binding(1) var<storage, read_write> output: array<f32>;
///
/// @compute @workgroup_size(256)
/// fn main(@builtin(global_invocation_id) id: vec3<u32>) {
/// output[id.x] = input[id.x] * 2.0;
/// }
/// "#,
/// "main",
/// &[BindingDesc::storage_readonly(), BindingDesc::storage_readwrite()],
/// );
/// ```
pub fn from_shader(
device: &Device,
shader_source: &str,
entry_point: &str,
bindings: &[BindingDesc],
) -> GpuResult<Self> {
Self::from_shader_with_workgroup_size(
device,
shader_source,
entry_point,
bindings,
[DEFAULT_WORKGROUP_SIZE, 1, 1],
)
}
/// Create a pipeline with custom workgroup size.
pub fn from_shader_with_workgroup_size(
device: &Device,
shader_source: &str,
entry_point: &str,
bindings: &[BindingDesc],
workgroup_size: [u32; 3],
) -> GpuResult<Self> {
debug!(
"Creating compute pipeline with entry point '{}' and {} bindings",
entry_point,
bindings.len()
);
// Create shader module
let shader = device.create_shader_module(wgpu::ShaderModuleDescriptor {
label: Some("compute_shader"),
source: wgpu::ShaderSource::Wgsl(shader_source.into()),
});
Self::from_module(device, &shader, entry_point, bindings, workgroup_size)
}
/// Create a pipeline from a pre-compiled shader module.
pub fn from_module(
device: &Device,
shader: &ShaderModule,
entry_point: &str,
bindings: &[BindingDesc],
workgroup_size: [u32; 3],
) -> GpuResult<Self> {
// Create bind group layout entries
let layout_entries: Vec<wgpu::BindGroupLayoutEntry> = bindings
.iter()
.enumerate()
.map(|(i, desc)| wgpu::BindGroupLayoutEntry {
binding: i as u32,
visibility: wgpu::ShaderStages::COMPUTE,
ty: desc.binding_type.to_wgpu(),
count: None,
})
.collect();
// Create bind group layout
let bind_group_layout = device.create_bind_group_layout(&wgpu::BindGroupLayoutDescriptor {
label: Some("compute_bind_group_layout"),
entries: &layout_entries,
});
// Create pipeline layout
let pipeline_layout = device.create_pipeline_layout(&wgpu::PipelineLayoutDescriptor {
label: Some("compute_pipeline_layout"),
bind_group_layouts: &[&bind_group_layout],
push_constant_ranges: &[],
});
// Create compute pipeline
let pipeline = device.create_compute_pipeline(&wgpu::ComputePipelineDescriptor {
label: Some("compute_pipeline"),
layout: Some(&pipeline_layout),
module: shader,
entry_point: Some(entry_point),
compilation_options: wgpu::PipelineCompilationOptions::default(),
cache: None,
});
Ok(Self {
pipeline,
bind_group_layout,
workgroup_size,
entry_point: entry_point.to_string(),
binding_count: bindings.len(),
})
}
/// Create a bind group for this pipeline.
///
/// # Arguments
///
/// * `device` - The wgpu device
/// * `buffers` - Buffers to bind, in order
///
/// # Panics
///
/// Panics if the number of buffers doesn't match the pipeline's binding count.
pub fn create_bind_group(
&self,
device: &Device,
buffers: &[&GpuBuffer],
) -> GpuResult<wgpu::BindGroup> {
if buffers.len() != self.binding_count {
return Err(GpuError::InvalidBindingCount {
expected: self.binding_count,
actual: buffers.len(),
});
}
let entries: Vec<wgpu::BindGroupEntry> = buffers
.iter()
.enumerate()
.map(|(i, buffer)| buffer.binding(i as u32))
.collect();
Ok(device.create_bind_group(&wgpu::BindGroupDescriptor {
label: Some("compute_bind_group"),
layout: &self.bind_group_layout,
entries: &entries,
}))
}
/// Get the underlying wgpu pipeline
pub fn pipeline(&self) -> &wgpu::ComputePipeline {
&self.pipeline
}
/// Get the bind group layout
pub fn bind_group_layout(&self) -> &wgpu::BindGroupLayout {
&self.bind_group_layout
}
/// Get the workgroup size
pub fn workgroup_size(&self) -> [u32; 3] {
self.workgroup_size
}
/// Get the entry point name
pub fn entry_point(&self) -> &str {
&self.entry_point
}
/// Get the number of bindings
pub fn binding_count(&self) -> usize {
self.binding_count
}
/// Calculate workgroup count for a given data size.
pub fn calculate_workgroups(&self, data_size: u32) -> [u32; 3] {
let x = (data_size + self.workgroup_size[0] - 1) / self.workgroup_size[0];
[x, 1, 1]
}
/// Calculate workgroup count for 2D data.
pub fn calculate_workgroups_2d(&self, width: u32, height: u32) -> [u32; 3] {
let x = (width + self.workgroup_size[0] - 1) / self.workgroup_size[0];
let y = (height + self.workgroup_size[1] - 1) / self.workgroup_size[1];
[x, y, 1]
}
/// Calculate workgroup count for 3D data.
pub fn calculate_workgroups_3d(&self, width: u32, height: u32, depth: u32) -> [u32; 3] {
let x = (width + self.workgroup_size[0] - 1) / self.workgroup_size[0];
let y = (height + self.workgroup_size[1] - 1) / self.workgroup_size[1];
let z = (depth + self.workgroup_size[2] - 1) / self.workgroup_size[2];
[x, y, z]
}
}
/// Cache for compute pipelines
pub struct PipelineCache {
device: Arc<Device>,
pipelines: DashMap<String, Arc<ComputePipeline>>,
}
impl PipelineCache {
/// Create a new pipeline cache
pub fn new(device: Arc<Device>) -> Self {
Self {
device,
pipelines: DashMap::new(),
}
}
/// Get or create a pipeline.
///
/// # Arguments
///
/// * `name` - Unique name for the pipeline
/// * `shader_source` - WGSL shader source
/// * `entry_point` - Entry point function name
/// * `bindings` - Binding descriptions
pub fn get_or_create(
&self,
name: &str,
shader_source: &str,
entry_point: &str,
bindings: &[BindingDesc],
) -> GpuResult<Arc<ComputePipeline>> {
if let Some(pipeline) = self.pipelines.get(name) {
return Ok(Arc::clone(&pipeline));
}
info!("Creating and caching pipeline: {}", name);
let pipeline = ComputePipeline::from_shader(&self.device, shader_source, entry_point, bindings)?;
let pipeline = Arc::new(pipeline);
self.pipelines.insert(name.to_string(), Arc::clone(&pipeline));
Ok(pipeline)
}
/// Get a cached pipeline by name.
pub fn get(&self, name: &str) -> Option<Arc<ComputePipeline>> {
self.pipelines.get(name).map(|p| Arc::clone(&p))
}
/// Check if a pipeline exists in cache.
pub fn contains(&self, name: &str) -> bool {
self.pipelines.contains_key(name)
}
/// Remove a pipeline from cache.
pub fn remove(&self, name: &str) -> Option<Arc<ComputePipeline>> {
self.pipelines.remove(name).map(|(_, p)| p)
}
/// Clear all cached pipelines.
pub fn clear(&self) {
self.pipelines.clear();
}
/// Get the number of cached pipelines.
pub fn len(&self) -> usize {
self.pipelines.len()
}
/// Check if the cache is empty.
pub fn is_empty(&self) -> bool {
self.pipelines.is_empty()
}
/// List all cached pipeline names.
pub fn names(&self) -> Vec<String> {
self.pipelines.iter().map(|e| e.key().clone()).collect()
}
}
/// Pre-defined shaders for common coherence operations
pub mod shaders {
/// WGSL shader for computing residuals
pub const RESIDUAL_COMPUTE: &str = r#"
// Node states: [node_count, dim]
@group(0) @binding(0) var<storage, read> node_states: array<f32>;
// Edge info: [edge_count, 4] - source_idx, target_idx, weight, padding
@group(0) @binding(1) var<storage, read> edges: array<vec4<f32>>;
// Restriction map (identity for simplicity): [dim, dim]
@group(0) @binding(2) var<storage, read> restriction: array<f32>;
// Output residuals: [edge_count]
@group(0) @binding(3) var<storage, read_write> residuals: array<f32>;
// Params: [dim, node_count, edge_count, 0]
@group(0) @binding(4) var<uniform> params: vec4<u32>;
@compute @workgroup_size(256)
fn main(@builtin(global_invocation_id) id: vec3<u32>) {
let edge_idx = id.x;
let edge_count = params.z;
let dim = params.x;
if (edge_idx >= edge_count) {
return;
}
let edge = edges[edge_idx];
let source_idx = u32(edge.x);
let target_idx = u32(edge.y);
let weight = edge.z;
// Compute residual = ||rho_u(x_u) - rho_v(x_v)||^2
var residual: f32 = 0.0;
for (var d: u32 = 0u; d < dim; d = d + 1u) {
let source_val = node_states[source_idx * dim + d];
let target_val = node_states[target_idx * dim + d];
let diff = source_val - target_val;
residual = residual + diff * diff;
}
residuals[edge_idx] = weight * residual;
}
"#;
/// WGSL shader for parallel reduction (sum)
pub const REDUCE_SUM: &str = r#"
@group(0) @binding(0) var<storage, read> input: array<f32>;
@group(0) @binding(1) var<storage, read_write> output: array<f32>;
@group(0) @binding(2) var<uniform> count: u32;
var<workgroup> shared_data: array<f32, 256>;
@compute @workgroup_size(256)
fn main(
@builtin(global_invocation_id) global_id: vec3<u32>,
@builtin(local_invocation_id) local_id: vec3<u32>,
@builtin(workgroup_id) workgroup_id: vec3<u32>
) {
let tid = local_id.x;
let gid = global_id.x;
// Load data into shared memory
if (gid < count) {
shared_data[tid] = input[gid];
} else {
shared_data[tid] = 0.0;
}
workgroupBarrier();
// Parallel reduction
for (var s: u32 = 128u; s > 0u; s = s >> 1u) {
if (tid < s) {
shared_data[tid] = shared_data[tid] + shared_data[tid + s];
}
workgroupBarrier();
}
// Write result
if (tid == 0u) {
output[workgroup_id.x] = shared_data[0];
}
}
"#;
/// WGSL shader for matrix-vector multiplication
pub const MATVEC: &str = r#"
@group(0) @binding(0) var<storage, read> matrix: array<f32>;
@group(0) @binding(1) var<storage, read> vector: array<f32>;
@group(0) @binding(2) var<storage, read_write> result: array<f32>;
// params: [rows, cols, 0, 0]
@group(0) @binding(3) var<uniform> params: vec4<u32>;
@compute @workgroup_size(256)
fn main(@builtin(global_invocation_id) id: vec3<u32>) {
let row = id.x;
let rows = params.x;
let cols = params.y;
if (row >= rows) {
return;
}
var sum: f32 = 0.0;
for (var c: u32 = 0u; c < cols; c = c + 1u) {
sum = sum + matrix[row * cols + c] * vector[c];
}
result[row] = sum;
}
"#;
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_binding_desc() {
let readonly = BindingDesc::storage_readonly();
assert_eq!(readonly.binding_type, BindingType::StorageReadonly);
let readwrite = BindingDesc::storage_readwrite();
assert_eq!(readwrite.binding_type, BindingType::StorageReadWrite);
let uniform = BindingDesc::uniform();
assert_eq!(uniform.binding_type, BindingType::Uniform);
}
#[test]
fn test_binding_with_label() {
let binding = BindingDesc::storage_readonly().with_label("input_buffer");
assert_eq!(binding.label.as_deref(), Some("input_buffer"));
}
}

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// =============================================================================
// Prime-Radiant GPU Compute Shaders - Energy Computation
// =============================================================================
//
// Parallel reduction to compute total coherence energy:
// E(S) = sum(w_e * |r_e|^2)
//
// Uses a two-phase reduction strategy:
// 1. Local reduction within workgroups using shared memory
// 2. Global reduction across workgroup partial sums
// =============================================================================
// TYPE DEFINITIONS
// =============================================================================
struct EnergyParams {
num_elements: u32,
_padding0: u32,
_padding1: u32,
_padding2: u32,
_padding3: u32,
_padding4: u32,
_padding5: u32,
_padding6: u32,
}
const WORKGROUP_SIZE: u32 = 256u;
// =============================================================================
// BUFFER BINDINGS
// =============================================================================
// Layout matches Rust kernel bind group:
// binding 0: params (uniform)
// binding 1: input (storage, read) - edge energies or partial sums
// binding 2: output (storage, read_write) - partial sums or final result
/// Energy computation parameters
@group(0) @binding(0) var<uniform> params: EnergyParams;
/// Input values to reduce
@group(0) @binding(1) var<storage, read> input_values: array<f32>;
/// Output partial sums or final result
@group(0) @binding(2) var<storage, read_write> output_values: array<f32>;
// =============================================================================
// SHARED MEMORY
// =============================================================================
/// Shared memory for parallel reduction
var<workgroup> shared_data: array<f32, 256>;
// =============================================================================
// MAIN REDUCTION KERNEL
// =============================================================================
/// Phase 1: Reduce input values within workgroup
@compute @workgroup_size(256)
fn main(
@builtin(global_invocation_id) global_id: vec3<u32>,
@builtin(local_invocation_id) local_id: vec3<u32>,
@builtin(workgroup_id) workgroup_id: vec3<u32>
) {
let tid = local_id.x;
let gid = global_id.x;
let element_count = params.num_elements;
// Load element (or 0 if out of bounds)
var val: f32 = 0.0;
if (gid < element_count) {
val = input_values[gid];
}
// Store in shared memory
shared_data[tid] = val;
workgroupBarrier();
// Tree reduction with sequential addressing
for (var stride = WORKGROUP_SIZE / 2u; stride > 0u; stride >>= 1u) {
if (tid < stride) {
shared_data[tid] += shared_data[tid + stride];
}
workgroupBarrier();
}
// Thread 0 writes the partial sum
if (tid == 0u) {
output_values[workgroup_id.x] = shared_data[0];
}
}
// =============================================================================
// FINAL REDUCTION PASS
// =============================================================================
/// Phase 2: Reduce partial sums to final total
/// Reads from input_values (the partial sums from phase 1)
/// Writes result to output_values[0]
@compute @workgroup_size(256)
fn final_reduce(
@builtin(local_invocation_id) local_id: vec3<u32>
) {
let tid = local_id.x;
let element_count = params.num_elements;
// Load partial sum from input (or 0 if out of bounds)
var sum: f32 = 0.0;
if (tid < element_count) {
sum = input_values[tid];
}
// Handle case where we have more partial sums than workgroup size
var idx = tid + WORKGROUP_SIZE;
while (idx < element_count) {
sum += input_values[idx];
idx += WORKGROUP_SIZE;
}
shared_data[tid] = sum;
workgroupBarrier();
// Tree reduction
for (var stride = WORKGROUP_SIZE / 2u; stride > 0u; stride >>= 1u) {
if (tid < stride) {
shared_data[tid] += shared_data[tid + stride];
}
workgroupBarrier();
}
// Write final result to output[0]
if (tid == 0u) {
output_values[0] = shared_data[0];
}
}

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// =============================================================================
// Prime-Radiant GPU Compute Shaders - Residual Computation
// =============================================================================
//
// Computes sheaf Laplacian residuals: r_e = rho_source(x_source) - rho_target(x_target)
// and per-edge energy: E_e = w_e * ||r_e||^2
//
// Each thread processes one edge, computing the residual and squared norm.
// =============================================================================
// TYPE DEFINITIONS (must match Rust structs exactly)
// =============================================================================
struct GpuParams {
num_edges: u32,
num_nodes: u32,
state_dim: u32,
beta: f32,
threshold_lane0: f32,
threshold_lane1: f32,
threshold_lane2: f32,
_padding: u32,
}
struct GpuEdge {
source_idx: u32,
target_idx: u32,
weight: f32,
rho_source_idx: u32,
rho_target_idx: u32,
comparison_dim: u32,
_padding0: u32,
_padding1: u32,
}
struct GpuRestrictionMap {
map_type: u32, // 0=identity, 1=diagonal, 2=projection, 3=dense
input_dim: u32,
output_dim: u32,
data_offset: u32,
data_len: u32,
_padding0: u32,
_padding1: u32,
_padding2: u32,
}
const WORKGROUP_SIZE: u32 = 256u;
const MAP_IDENTITY: u32 = 0u;
const MAP_DIAGONAL: u32 = 1u;
const MAP_PROJECTION: u32 = 2u;
const MAP_DENSE: u32 = 3u;
// =============================================================================
// BUFFER BINDINGS (matches Rust kernel bind group layout)
// =============================================================================
// binding 0: params (uniform)
// binding 1: node_states (storage, read)
// binding 2: edges (storage, read)
// binding 3: restriction_maps (storage, read)
// binding 4: restriction_data (storage, read)
// binding 5: residuals (storage, read_write)
// binding 6: energies (storage, read_write)
@group(0) @binding(0) var<uniform> params: GpuParams;
@group(0) @binding(1) var<storage, read> node_states: array<f32>;
@group(0) @binding(2) var<storage, read> edges: array<GpuEdge>;
@group(0) @binding(3) var<storage, read> restriction_maps: array<GpuRestrictionMap>;
@group(0) @binding(4) var<storage, read> restriction_data: array<f32>;
@group(0) @binding(5) var<storage, read_write> residuals: array<f32>;
@group(0) @binding(6) var<storage, read_write> energies: array<f32>;
// =============================================================================
// HELPER FUNCTIONS
// =============================================================================
/// Apply restriction map to a state vector at the given offset
/// Returns the projected value at output dimension d
fn apply_restriction(
rho: GpuRestrictionMap,
state_base: u32,
output_dim: u32
) -> f32 {
switch(rho.map_type) {
case MAP_IDENTITY: {
// Identity: just return the corresponding element
if (output_dim < rho.output_dim && output_dim < params.state_dim) {
return node_states[state_base + output_dim];
}
return 0.0;
}
case MAP_DIAGONAL: {
// Diagonal: scale by diagonal element
if (output_dim < rho.data_len) {
let scale = restriction_data[rho.data_offset + output_dim];
return node_states[state_base + output_dim] * scale;
}
return 0.0;
}
case MAP_PROJECTION: {
// Projection: select specific indices
if (output_dim < rho.data_len) {
let idx = u32(restriction_data[rho.data_offset + output_dim]);
if (idx < params.state_dim) {
return node_states[state_base + idx];
}
}
return 0.0;
}
case MAP_DENSE, default: {
// Dense: matrix-vector multiply for row output_dim
var result: f32 = 0.0;
let row_offset = rho.data_offset + output_dim * rho.input_dim;
for (var i = 0u; i < rho.input_dim && i < params.state_dim; i++) {
result += restriction_data[row_offset + i] * node_states[state_base + i];
}
return result;
}
}
return 0.0;
}
// =============================================================================
// MAIN ENTRY POINT
// =============================================================================
@compute @workgroup_size(256)
fn main(
@builtin(global_invocation_id) global_id: vec3<u32>
) {
let edge_idx = global_id.x;
// Bounds check
if (edge_idx >= params.num_edges) {
return;
}
// Get edge data
let edge = edges[edge_idx];
// Compute base offsets for source and target node states
let source_base = edge.source_idx * params.state_dim;
let target_base = edge.target_idx * params.state_dim;
// Get restriction maps
let rho_source = restriction_maps[edge.rho_source_idx];
let rho_target = restriction_maps[edge.rho_target_idx];
// Compute residual: r = rho_source(x_source) - rho_target(x_target)
// and accumulate squared norm
var norm_sq: f32 = 0.0;
let comparison_dim = edge.comparison_dim;
let residual_base = edge_idx * comparison_dim;
for (var d = 0u; d < comparison_dim; d++) {
// Apply restriction maps
let projected_source = apply_restriction(rho_source, source_base, d);
let projected_target = apply_restriction(rho_target, target_base, d);
// Compute residual component
let r = projected_source - projected_target;
// Store residual (optional - can be skipped if only energy needed)
if (residual_base + d < arrayLength(&residuals)) {
residuals[residual_base + d] = r;
}
// Accumulate squared norm
norm_sq += r * r;
}
// Compute weighted energy: E_e = w_e * ||r_e||^2
let energy = edge.weight * norm_sq;
// Store per-edge energy
energies[edge_idx] = energy;
}

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// =============================================================================
// Prime-Radiant GPU Compute Shaders - Sheaf Attention
// =============================================================================
//
// Energy-based sheaf attention: A_ij = softmax(-beta * E_ij)
//
// Attention weights are computed from coherence energy:
// - Low energy (coherent) edges get high attention
// - High energy (incoherent) edges get low attention
// =============================================================================
// TYPE DEFINITIONS
// =============================================================================
struct AttentionParams {
num_edges: u32,
num_nodes: u32,
beta: f32,
energy_threshold: f32,
use_sparse: u32,
_padding0: u32,
_padding1: u32,
_padding2: u32,
}
struct EdgeDescriptor {
source_idx: u32,
target_idx: u32,
weight: f32,
_padding: u32,
}
const WORKGROUP_SIZE: u32 = 256u;
const NEG_INF: f32 = -3.402823e+38;
const EPSILON: f32 = 1e-8;
// =============================================================================
// BUFFER BINDINGS
// =============================================================================
// Layout matches Rust kernel bind group:
// binding 0: params (uniform)
// binding 1: edges (storage, read)
// binding 2: edge_energies (storage, read)
// binding 3: attention_weights (storage, read_write)
// binding 4: node_exp_sums (storage, read_write)
/// Attention parameters
@group(0) @binding(0) var<uniform> params: AttentionParams;
/// Edge descriptors
@group(0) @binding(1) var<storage, read> edges: array<EdgeDescriptor>;
/// Edge energies from residual computation
@group(0) @binding(2) var<storage, read> edge_energies: array<f32>;
/// Output attention weights (one per edge)
@group(0) @binding(3) var<storage, read_write> attention_weights: array<f32>;
/// Per-node exponential sums for normalization
@group(0) @binding(4) var<storage, read_write> node_exp_sums: array<f32>;
// =============================================================================
// SHARED MEMORY
// =============================================================================
/// Shared memory for parallel reduction
var<workgroup> shared_data: array<f32, 256>;
// =============================================================================
// SINGLE-PASS ATTENTION COMPUTATION
// =============================================================================
/// Compute attention weights from edge energies
/// A_e = exp(-beta * E_e) (unnormalized)
/// Each workgroup processes multiple edges
@compute @workgroup_size(256)
fn compute_attention_single_pass(
@builtin(global_invocation_id) global_id: vec3<u32>,
@builtin(local_invocation_id) local_id: vec3<u32>
) {
let edge_idx = global_id.x;
let num_edges = params.num_edges;
let beta = params.beta;
if (edge_idx >= num_edges) {
return;
}
// Get edge energy
let energy = edge_energies[edge_idx];
// Compute unnormalized attention weight
// For energy-based attention: A = exp(-beta * E)
// High energy (incoherent) -> low attention
// Low energy (coherent) -> high attention
var score = -beta * energy;
// Apply energy threshold masking for sparse attention
if (params.use_sparse == 1u && energy > params.energy_threshold) {
score = NEG_INF;
}
// Compute exp(score) - clamp to avoid overflow
let clamped_score = clamp(score, -80.0, 80.0);
let exp_score = exp(clamped_score);
// Store unnormalized attention weight
attention_weights[edge_idx] = exp_score;
// Accumulate exp sum for source node (for later normalization)
// Note: This requires atomic operations for correctness in parallel
// For now, we store unnormalized weights; normalization done in separate pass
let edge = edges[edge_idx];
// atomicAdd(&node_exp_sums[edge.source_idx], exp_score);
// Note: WGSL doesn't have atomicAdd for f32, so we store for CPU normalization
}
// =============================================================================
// NORMALIZATION PASS
// =============================================================================
/// Normalize attention weights by node (outgoing edges sum to 1)
/// Second pass after exp sums are computed
@compute @workgroup_size(256)
fn normalize_attention(
@builtin(global_invocation_id) global_id: vec3<u32>
) {
let edge_idx = global_id.x;
let num_edges = params.num_edges;
if (edge_idx >= num_edges) {
return;
}
let edge = edges[edge_idx];
let source_idx = edge.source_idx;
// Get the sum of exp scores for this source node
let exp_sum = node_exp_sums[source_idx];
// Normalize
let normalized = attention_weights[edge_idx] / max(exp_sum, EPSILON);
attention_weights[edge_idx] = normalized;
}

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// =============================================================================
// Prime-Radiant GPU Compute Shaders - Sparse Attention Mask
// =============================================================================
//
// Generate sparse attention masks from energy thresholds.
// Only edges with energy below threshold (coherent) are included.
//
// This enables efficient sparse attention where only meaningful
// (low-energy, coherent) connections are computed, dramatically
// reducing computation for large graphs.
//
// Output Formats:
// 1. Index list: Compact list of (row, col) pairs for valid edges
// 2. Dense mask: Full NxN boolean matrix (for small N)
// 3. CSR format: Compressed sparse row for efficient sparse matmul
//
// Optimizations:
// - Stream compaction for index list generation
// - Warp-level voting for efficient counting
// - Coalesced writes using shared memory staging
// =============================================================================
// TYPE DEFINITIONS
// =============================================================================
struct SparseMaskParams {
total_edges: u32,
coherence_threshold: f32,
max_edges: u32,
output_format: u32, // 0=indices, 1=dense, 2=csr
seq_len: u32,
batch_size: u32,
padding: array<u32, 2>,
}
struct EdgeIndex {
row: u32,
col: u32,
}
struct CSRPointers {
row_ptr: u32,
nnz: u32,
}
const WORKGROUP_SIZE: u32 = 256u;
const OUTPUT_INDICES: u32 = 0u;
const OUTPUT_DENSE: u32 = 1u;
const OUTPUT_CSR: u32 = 2u;
// =============================================================================
// BUFFER BINDINGS
// =============================================================================
/// Input edge energies (seq_len * seq_len per batch, or sparse)
@group(0) @binding(0) var<storage, read> edge_energies: array<f32>;
/// Output: sparse edge indices (for index format)
@group(0) @binding(1) var<storage, read_write> sparse_indices: array<EdgeIndex>;
/// Output: dense mask (for dense format)
@group(0) @binding(2) var<storage, read_write> dense_mask: array<u32>;
/// Output: number of valid edges (atomic counter)
@group(0) @binding(3) var<storage, read_write> edge_count: atomic<u32>;
/// Mask parameters
@group(0) @binding(4) var<uniform> params: SparseMaskParams;
// =============================================================================
// SHARED MEMORY
// =============================================================================
/// Shared memory for stream compaction
var<workgroup> shared_valid: array<u32, 256>;
/// Prefix sum for compaction offsets
var<workgroup> shared_prefix: array<u32, 256>;
/// Staging buffer for coalesced writes
var<workgroup> shared_indices: array<EdgeIndex, 256>;
/// Workgroup-level count of valid edges
var<workgroup> workgroup_count: atomic<u32>;
// =============================================================================
// BASIC SPARSE MASK GENERATION
// =============================================================================
/// Generate sparse mask as index list
@compute @workgroup_size(256)
fn generate_sparse_indices(
@builtin(global_invocation_id) global_id: vec3<u32>,
@builtin(local_invocation_id) local_id: vec3<u32>,
@builtin(workgroup_id) workgroup_id: vec3<u32>
) {
let idx = global_id.x;
let tid = local_id.x;
let total_edges = params.total_edges;
let threshold = params.coherence_threshold;
let seq_len = params.seq_len;
// Initialize workgroup counter
if (tid == 0u) {
atomicStore(&workgroup_count, 0u);
}
workgroupBarrier();
// Check if this edge is valid (below threshold)
var is_valid: u32 = 0u;
var row: u32 = 0u;
var col: u32 = 0u;
if (idx < total_edges) {
let energy = edge_energies[idx];
is_valid = select(0u, 1u, energy < threshold);
// Compute row and column from linear index
row = idx / seq_len;
col = idx % seq_len;
}
shared_valid[tid] = is_valid;
workgroupBarrier();
// Compute prefix sum for compaction
// Hillis-Steele parallel scan
shared_prefix[tid] = is_valid;
workgroupBarrier();
for (var offset = 1u; offset < WORKGROUP_SIZE; offset <<= 1u) {
var val: u32 = 0u;
if (tid >= offset) {
val = shared_prefix[tid - offset];
}
workgroupBarrier();
shared_prefix[tid] += val;
workgroupBarrier();
}
// Total valid in this workgroup
let total_valid = shared_prefix[WORKGROUP_SIZE - 1u];
// Get global offset for this workgroup
var global_offset: u32 = 0u;
if (tid == 0u && total_valid > 0u) {
global_offset = atomicAdd(&edge_count, total_valid);
atomicStore(&workgroup_count, global_offset);
}
workgroupBarrier();
global_offset = atomicLoad(&workgroup_count);
// Write valid edges to output using compacted indices
if (is_valid == 1u && idx < total_edges) {
// Exclusive prefix sum gives position
let local_pos = select(0u, shared_prefix[tid - 1u], tid > 0u);
let global_pos = global_offset + local_pos;
if (global_pos < params.max_edges) {
sparse_indices[global_pos] = EdgeIndex(row, col);
}
}
}
// =============================================================================
// DENSE MASK GENERATION
// =============================================================================
/// Generate dense boolean mask (packed as u32 bits)
@compute @workgroup_size(256)
fn generate_dense_mask(
@builtin(global_invocation_id) global_id: vec3<u32>
) {
let idx = global_id.x;
let total_edges = params.total_edges;
let threshold = params.coherence_threshold;
if (idx >= total_edges) {
return;
}
let energy = edge_energies[idx];
let is_valid = energy < threshold;
// Pack 32 boolean values per u32
let word_idx = idx / 32u;
let bit_idx = idx % 32u;
if (is_valid) {
// Atomic OR to set the bit
atomicOr(&dense_mask[word_idx], 1u << bit_idx);
}
}
/// Unpack dense mask bit
fn is_edge_valid(dense_mask_ptr: ptr<storage, array<u32>, read>, idx: u32) -> bool {
let word_idx = idx / 32u;
let bit_idx = idx % 32u;
return ((*dense_mask_ptr)[word_idx] & (1u << bit_idx)) != 0u;
}
// =============================================================================
// CSR FORMAT GENERATION
// =============================================================================
/// CSR row pointers
@group(1) @binding(0) var<storage, read_write> csr_row_ptr: array<u32>;
/// CSR column indices
@group(1) @binding(1) var<storage, read_write> csr_col_idx: array<u32>;
/// CSR values (attention weights or energies)
@group(1) @binding(2) var<storage, read_write> csr_values: array<f32>;
/// Per-row counters for CSR construction
@group(1) @binding(3) var<storage, read_write> row_counts: array<atomic<u32>>;
/// Phase 1: Count valid edges per row
@compute @workgroup_size(256)
fn count_edges_per_row(
@builtin(global_invocation_id) global_id: vec3<u32>
) {
let idx = global_id.x;
let total_edges = params.total_edges;
let threshold = params.coherence_threshold;
let seq_len = params.seq_len;
if (idx >= total_edges) {
return;
}
let energy = edge_energies[idx];
if (energy < threshold) {
let row = idx / seq_len;
atomicAdd(&row_counts[row], 1u);
}
}
/// Phase 2: Compute row pointers via prefix sum
@compute @workgroup_size(256)
fn compute_row_pointers(
@builtin(global_invocation_id) global_id: vec3<u32>,
@builtin(local_invocation_id) local_id: vec3<u32>
) {
let row = global_id.x;
let tid = local_id.x;
let seq_len = params.seq_len;
if (row >= seq_len) {
return;
}
// Load count into shared memory
shared_prefix[tid] = atomicLoad(&row_counts[row]);
workgroupBarrier();
// Inclusive prefix sum
for (var offset = 1u; offset < WORKGROUP_SIZE; offset <<= 1u) {
var val: u32 = 0u;
if (tid >= offset) {
val = shared_prefix[tid - offset];
}
workgroupBarrier();
shared_prefix[tid] += val;
workgroupBarrier();
}
// Convert to exclusive prefix sum for row pointers
// row_ptr[i] = sum of counts for rows 0..i-1
let inclusive_sum = shared_prefix[tid];
let count = atomicLoad(&row_counts[row]);
let exclusive_sum = inclusive_sum - count;
csr_row_ptr[row] = exclusive_sum;
// Reset counter to be used as write position
atomicStore(&row_counts[row], exclusive_sum);
// Last row sets the final pointer (total nnz)
if (row == seq_len - 1u) {
csr_row_ptr[seq_len] = inclusive_sum;
}
}
/// Phase 3: Populate CSR column indices and values
@compute @workgroup_size(256)
fn populate_csr_data(
@builtin(global_invocation_id) global_id: vec3<u32>
) {
let idx = global_id.x;
let total_edges = params.total_edges;
let threshold = params.coherence_threshold;
let seq_len = params.seq_len;
if (idx >= total_edges) {
return;
}
let energy = edge_energies[idx];
if (energy < threshold) {
let row = idx / seq_len;
let col = idx % seq_len;
// Get write position using atomic increment
let pos = atomicAdd(&row_counts[row], 1u);
csr_col_idx[pos] = col;
csr_values[pos] = energy;
}
}
// =============================================================================
// BATCHED SPARSE MASK
// =============================================================================
/// Batch offsets for multi-batch processing
@group(2) @binding(0) var<storage, read> batch_offsets: array<u32>;
/// Per-batch edge counts
@group(2) @binding(1) var<storage, read_write> batch_edge_counts: array<atomic<u32>>;
/// Generate sparse mask for multiple batches
@compute @workgroup_size(256)
fn generate_batched_sparse_mask(
@builtin(global_invocation_id) global_id: vec3<u32>,
@builtin(local_invocation_id) local_id: vec3<u32>,
@builtin(workgroup_id) workgroup_id: vec3<u32>
) {
let batch_idx = workgroup_id.z;
let local_idx = global_id.x;
let tid = local_id.x;
let seq_len = params.seq_len;
let edges_per_batch = seq_len * seq_len;
let threshold = params.coherence_threshold;
if (local_idx >= edges_per_batch) {
return;
}
// Global index in energy array
let global_idx = batch_idx * edges_per_batch + local_idx;
let energy = edge_energies[global_idx];
let is_valid = select(0u, 1u, energy < threshold);
// Stream compaction within batch
shared_valid[tid] = is_valid;
workgroupBarrier();
// Prefix sum
shared_prefix[tid] = is_valid;
workgroupBarrier();
for (var offset = 1u; offset < WORKGROUP_SIZE; offset <<= 1u) {
var val: u32 = 0u;
if (tid >= offset) {
val = shared_prefix[tid - offset];
}
workgroupBarrier();
shared_prefix[tid] += val;
workgroupBarrier();
}
// Get batch-local offset
if (tid == 0u) {
let total_valid = shared_prefix[WORKGROUP_SIZE - 1u];
let offset = atomicAdd(&batch_edge_counts[batch_idx], total_valid);
atomicStore(&workgroup_count, offset);
}
workgroupBarrier();
let batch_offset = batch_offsets[batch_idx];
let workgroup_offset = atomicLoad(&workgroup_count);
// Write valid edges
if (is_valid == 1u) {
let local_pos = select(0u, shared_prefix[tid - 1u], tid > 0u);
let global_pos = batch_offset + workgroup_offset + local_pos;
let row = local_idx / seq_len;
let col = local_idx % seq_len;
if (global_pos < params.max_edges) {
sparse_indices[global_pos] = EdgeIndex(row, col);
}
}
}
// =============================================================================
// DYNAMIC THRESHOLD ADJUSTMENT
// =============================================================================
/// Statistics for adaptive threshold
@group(3) @binding(0) var<storage, read_write> mask_stats: array<f32>;
/// Compute mask statistics for adaptive thresholding
@compute @workgroup_size(256)
fn compute_mask_statistics(
@builtin(global_invocation_id) global_id: vec3<u32>,
@builtin(local_invocation_id) local_id: vec3<u32>
) {
let idx = global_id.x;
let tid = local_id.x;
let total_edges = params.total_edges;
let threshold = params.coherence_threshold;
// Count valid and total, compute sparsity ratio
var valid_count: u32 = 0u;
if (idx < total_edges) {
let energy = edge_energies[idx];
valid_count = select(0u, 1u, energy < threshold);
}
shared_prefix[tid] = valid_count;
workgroupBarrier();
// Reduce to get total valid
for (var stride = WORKGROUP_SIZE / 2u; stride > 0u; stride >>= 1u) {
if (tid < stride) {
shared_prefix[tid] += shared_prefix[tid + stride];
}
workgroupBarrier();
}
// Thread 0 updates global statistics
if (tid == 0u) {
// Atomic add to global counter
// mask_stats[0] = total valid edges
// mask_stats[1] = sparsity ratio (computed after all workgroups)
}
}
// =============================================================================
// CAUSAL MASK COMBINATION
// =============================================================================
/// Combine energy-based sparse mask with causal mask
@compute @workgroup_size(16, 16)
fn combine_with_causal_mask(
@builtin(global_invocation_id) global_id: vec3<u32>
) {
let row = global_id.y;
let col = global_id.x;
let seq_len = params.seq_len;
let threshold = params.coherence_threshold;
if (row >= seq_len || col >= seq_len) {
return;
}
let idx = row * seq_len + col;
let energy = edge_energies[idx];
// Valid if: (1) below energy threshold AND (2) satisfies causal constraint
let energy_valid = energy < threshold;
let causal_valid = col <= row; // Can only attend to past
let is_valid = energy_valid && causal_valid;
// Write to dense mask
let word_idx = idx / 32u;
let bit_idx = idx % 32u;
if (is_valid) {
atomicOr(&dense_mask[word_idx], 1u << bit_idx);
}
}

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@ -0,0 +1,253 @@
// =============================================================================
// Prime-Radiant GPU Compute Shaders - Token Routing
// =============================================================================
//
// Parallel lane assignment for tokens based on coherence energy thresholds.
// Routes tokens to different processing lanes (experts) based on their
// local coherence energy, enabling adaptive computation.
//
// Lane Semantics:
// - Lane 0: Coherent (energy < tau_0) - Fast path, minimal processing
// - Lane 1: Semi-coherent (tau_0 <= energy < tau_1) - Normal processing
// - Lane 2: Incoherent (tau_1 <= energy < tau_2) - Enhanced processing
// - Lane 3: Critical (energy >= tau_2) - Special handling required
// =============================================================================
// TYPE DEFINITIONS
// =============================================================================
struct RoutingParams {
num_tokens: u32,
num_nodes: u32,
threshold_0: f32,
threshold_1: f32,
threshold_2: f32,
high_energy_threshold: f32,
_padding0: u32,
_padding1: u32,
}
struct Token {
token_id: u32,
node_idx: u32,
action_type: u32,
priority: f32,
}
struct RoutingDecision {
token_id: u32,
assigned_lane: u32,
local_energy: f32,
confidence: f32,
escalation_reason: u32,
num_high_energy_edges: u32,
max_edge_energy: f32,
_padding: u32,
}
struct LaneStats {
lane_counts: vec4<u32>,
total_energy_per_lane: vec4<f32>,
_padding: array<u32, 8>,
}
const WORKGROUP_SIZE: u32 = 256u;
const NUM_LANES: u32 = 4u;
// =============================================================================
// BUFFER BINDINGS
// =============================================================================
// Layout matches Rust kernel bind group:
// binding 0: params (uniform)
// binding 1: tokens (storage, read)
// binding 2: local_energies (storage, read)
// binding 3: edge_energies (storage, read)
// binding 4: node_edge_counts (storage, read)
// binding 5: node_edge_offsets (storage, read)
// binding 6: node_edges (storage, read)
// binding 7: routing_decisions (storage, read_write)
// binding 8: lane_stats (storage, read_write)
/// Routing parameters
@group(0) @binding(0) var<uniform> params: RoutingParams;
/// Input tokens
@group(0) @binding(1) var<storage, read> tokens: array<Token>;
/// Pre-computed local energies per node
@group(0) @binding(2) var<storage, read> local_energies: array<f32>;
/// All edge energies
@group(0) @binding(3) var<storage, read> edge_energies: array<f32>;
/// Number of edges per node (CSR format)
@group(0) @binding(4) var<storage, read> node_edge_counts: array<u32>;
/// Edge start offsets per node (CSR format)
@group(0) @binding(5) var<storage, read> node_edge_offsets: array<u32>;
/// Edge indices per node (CSR format)
@group(0) @binding(6) var<storage, read> node_edges: array<u32>;
/// Output routing decisions
@group(0) @binding(7) var<storage, read_write> routing_decisions: array<RoutingDecision>;
/// Output lane statistics
@group(0) @binding(8) var<storage, read_write> lane_stats: LaneStats;
// =============================================================================
// SHARED MEMORY
// =============================================================================
/// Lane counts for workgroup-level reduction
var<workgroup> shared_lane_counts: array<atomic<u32>, 4>;
/// Lane energy sums for workgroup-level reduction
var<workgroup> shared_lane_energies: array<f32, 4>;
// =============================================================================
// HELPER FUNCTIONS
// =============================================================================
/// Branchless lane computation using step functions
fn compute_lane_branchless(energy: f32, t0: f32, t1: f32, t2: f32) -> u32 {
let s0 = select(0u, 1u, energy >= t0);
let s1 = select(0u, 1u, energy >= t1);
let s2 = select(0u, 1u, energy >= t2);
return s0 + s1 + s2;
}
/// Compute routing confidence based on how close energy is to threshold boundaries
fn compute_confidence(energy: f32, lane: u32, t0: f32, t1: f32, t2: f32) -> f32 {
// Confidence is based on distance from nearest threshold
var dist_to_threshold: f32;
switch(lane) {
case 0u: {
dist_to_threshold = t0 - energy;
}
case 1u: {
dist_to_threshold = min(energy - t0, t1 - energy);
}
case 2u: {
dist_to_threshold = min(energy - t1, t2 - energy);
}
case 3u, default: {
dist_to_threshold = energy - t2;
}
}
// Normalize to [0, 1] - higher means further from boundary
return clamp(dist_to_threshold * 10.0, 0.0, 1.0);
}
// =============================================================================
// MAIN ROUTING KERNEL
// =============================================================================
/// Route tokens to processing lanes based on local coherence energy
@compute @workgroup_size(256)
fn route_tokens(
@builtin(global_invocation_id) global_id: vec3<u32>,
@builtin(local_invocation_id) local_id: vec3<u32>,
@builtin(workgroup_id) workgroup_id: vec3<u32>
) {
let token_idx = global_id.x;
let local_idx = local_id.x;
let num_tokens = params.num_tokens;
// Initialize shared counters (first thread only)
if (local_idx == 0u) {
atomicStore(&shared_lane_counts[0], 0u);
atomicStore(&shared_lane_counts[1], 0u);
atomicStore(&shared_lane_counts[2], 0u);
atomicStore(&shared_lane_counts[3], 0u);
shared_lane_energies[0] = 0.0;
shared_lane_energies[1] = 0.0;
shared_lane_energies[2] = 0.0;
shared_lane_energies[3] = 0.0;
}
workgroupBarrier();
if (token_idx >= num_tokens) {
return;
}
let token = tokens[token_idx];
let node_idx = token.node_idx;
// Get local energy for this node
let local_energy = local_energies[node_idx];
// Compute lane assignment
let lane = compute_lane_branchless(
local_energy,
params.threshold_0,
params.threshold_1,
params.threshold_2
);
// Compute confidence
let confidence = compute_confidence(
local_energy,
lane,
params.threshold_0,
params.threshold_1,
params.threshold_2
);
// Analyze edges for this node
let edge_count = node_edge_counts[node_idx];
let edge_offset = node_edge_offsets[node_idx];
var num_high_energy_edges: u32 = 0u;
var max_edge_energy: f32 = 0.0;
var escalation_reason: u32 = 0u;
for (var i = 0u; i < edge_count; i++) {
let edge_idx = node_edges[edge_offset + i];
let edge_energy = edge_energies[edge_idx];
if (edge_energy > params.high_energy_threshold) {
num_high_energy_edges += 1u;
}
max_edge_energy = max(max_edge_energy, edge_energy);
}
// Determine if escalation is needed
if (num_high_energy_edges > 2u) {
escalation_reason = 1u; // Multiple high-energy edges
} else if (max_edge_energy > params.threshold_2) {
escalation_reason = 2u; // Single very high energy edge
}
// Write routing decision
var decision: RoutingDecision;
decision.token_id = token.token_id;
decision.assigned_lane = lane;
decision.local_energy = local_energy;
decision.confidence = confidence;
decision.escalation_reason = escalation_reason;
decision.num_high_energy_edges = num_high_energy_edges;
decision.max_edge_energy = max_edge_energy;
decision._padding = 0u;
routing_decisions[token_idx] = decision;
// Update lane statistics
atomicAdd(&shared_lane_counts[lane], 1u);
// Note: No atomic f32 add in WGSL, would need separate reduction pass
workgroupBarrier();
// First thread writes workgroup stats to global buffer
// (In production, would do proper atomic accumulation)
if (local_idx == 0u && workgroup_id.x == 0u) {
lane_stats.lane_counts = vec4<u32>(
atomicLoad(&shared_lane_counts[0]),
atomicLoad(&shared_lane_counts[1]),
atomicLoad(&shared_lane_counts[2]),
atomicLoad(&shared_lane_counts[3])
);
}
}

234
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@ -0,0 +1,234 @@
// =============================================================================
// Prime-Radiant GPU Compute Shaders - Shared Types
// =============================================================================
//
// This file contains shared struct definitions and constants used across
// all compute shaders in the Prime-Radiant coherence engine.
//
// Memory Layout:
// - All structs are aligned to 16 bytes for optimal GPU memory access
// - vec4<f32> is used where possible for coalesced memory operations
// - Padding fields ensure proper alignment
// =============================================================================
// COMPUTE PARAMETERS
// =============================================================================
/// Parameters for residual computation
struct ComputeParams {
/// Total number of edges to process
edge_count: u32,
/// Dimension of state vectors
state_dim: u32,
/// Restriction map type: 0=identity, 1=diagonal, 2=dense, 3=projection, 4=sparse
restriction_type: u32,
/// Padding for 16-byte alignment
padding: u32,
}
/// Parameters for parallel reduction operations
struct ReductionParams {
/// Number of elements to reduce
element_count: u32,
/// Stride between elements (for strided access patterns)
stride: u32,
/// Whether this is the final reduction pass
is_final_pass: u32,
/// Output offset for multi-pass reductions
output_offset: u32,
}
/// Parameters for attention computation
struct AttentionParams {
/// Batch size (number of independent attention operations)
batch_size: u32,
/// Sequence length (number of tokens/nodes)
seq_len: u32,
/// Dimension per attention head
head_dim: u32,
/// Inverse temperature parameter: A_ij = softmax(-beta * E_ij)
beta: f32,
/// Number of attention heads (for multi-head attention)
num_heads: u32,
/// Whether to use causal masking
use_causal_mask: u32,
/// Energy threshold for sparse attention (skip if E > threshold)
energy_threshold: f32,
/// Padding for 16-byte alignment
padding: u32,
}
/// Parameters for token routing
struct RoutingParams {
/// Number of tokens to route
token_count: u32,
/// Number of lanes/experts
num_lanes: u32,
/// Whether to use load balancing
use_load_balance: u32,
/// Top-k selection for MoE
top_k: u32,
}
/// Parameters for sparse mask generation
struct SparseMaskParams {
/// Total number of potential edges
total_edges: u32,
/// Energy threshold for coherence (keep edges below this)
coherence_threshold: f32,
/// Maximum edges to keep (for memory bounds)
max_edges: u32,
/// Output format: 0=indices, 1=dense mask
output_format: u32,
}
// =============================================================================
// EDGE AND NODE DATA STRUCTURES
// =============================================================================
/// Edge descriptor for graph connectivity (16-byte aligned)
struct EdgeDescriptor {
/// Index of source node
source_idx: u32,
/// Index of target node
target_idx: u32,
/// Offset into restriction data for this edge
restriction_offset: u32,
/// Weight for this edge
weight: f32,
}
/// Node state with metadata (16-byte aligned)
struct NodeState {
/// Offset into state buffer where this node's state begins
state_offset: u32,
/// Dimension of this node's state
state_dim: u32,
/// Scope ID for hierarchical energy aggregation
scope_id: u32,
/// Flags (bit 0: is_boundary, bit 1: is_fixed, etc.)
flags: u32,
}
/// Per-edge energy result (16-byte aligned)
struct EdgeEnergy {
/// Weighted energy: w_e * |r_e|^2
energy: f32,
/// Raw residual norm squared: |r_e|^2
residual_norm_sq: f32,
/// Edge weight that was applied
weight: f32,
/// Padding for alignment
padding: f32,
}
// =============================================================================
// ATTENTION STRUCTURES
// =============================================================================
/// Attention score for a single edge (16-byte aligned)
struct AttentionScore {
/// Source node index
source: u32,
/// Target node index
target: u32,
/// Attention weight (after softmax)
weight: f32,
/// Raw score (before softmax)
raw_score: f32,
}
/// Lane assignment result for token routing (16-byte aligned)
struct LaneAssignment {
/// Token index
token_idx: u32,
/// Assigned lane (0-3 typically)
lane: u32,
/// Confidence score for this assignment
confidence: f32,
/// Energy value that determined routing
energy: f32,
}
// =============================================================================
// CONSTANTS
// =============================================================================
/// Workgroup size for 1D dispatches
const WORKGROUP_SIZE_1D: u32 = 256u;
/// Workgroup dimensions for 2D dispatches (attention)
const WORKGROUP_SIZE_2D_X: u32 = 16u;
const WORKGROUP_SIZE_2D_Y: u32 = 16u;
/// Maximum supported state dimension (for stack allocation)
const MAX_STATE_DIM: u32 = 512u;
/// Epsilon for numerical stability
const EPSILON: f32 = 1e-8;
/// Negative infinity for softmax initialization
const NEG_INF: f32 = -3.402823e+38;
/// Restriction map type constants
const RESTRICTION_IDENTITY: u32 = 0u;
const RESTRICTION_DIAGONAL: u32 = 1u;
const RESTRICTION_DENSE: u32 = 2u;
const RESTRICTION_PROJECTION: u32 = 3u;
const RESTRICTION_SPARSE: u32 = 4u;
/// Lane thresholds for token routing (default values)
/// Lane 0: energy < 0.1 (coherent, fast path)
/// Lane 1: 0.1 <= energy < 0.5 (semi-coherent, normal path)
/// Lane 2: 0.5 <= energy < 1.0 (incoherent, slow path)
/// Lane 3: energy >= 1.0 (critical, special handling)
const DEFAULT_LANE_THRESHOLDS: vec4<f32> = vec4<f32>(0.1, 0.5, 1.0, 10.0);
// =============================================================================
// UTILITY FUNCTIONS
// =============================================================================
/// Compute squared L2 norm of a vec4
fn norm_sq_vec4(v: vec4<f32>) -> f32 {
return dot(v, v);
}
/// Safe division with epsilon
fn safe_div(a: f32, b: f32) -> f32 {
return a / max(b, EPSILON);
}
/// Branchless step function
fn step_branchless(threshold: f32, value: f32) -> f32 {
return select(0.0, 1.0, value >= threshold);
}
/// Compute lane index from energy using branchless comparison
fn compute_lane(energy: f32, thresholds: vec4<f32>) -> u32 {
return u32(step_branchless(thresholds.x, energy))
+ u32(step_branchless(thresholds.y, energy))
+ u32(step_branchless(thresholds.z, energy));
}
/// Online softmax helper - update max and sum
fn online_softmax_update(
old_max: f32,
old_sum: f32,
new_val: f32
) -> vec2<f32> {
let new_max = max(old_max, new_val);
let correction = exp(old_max - new_max);
let new_sum = old_sum * correction + exp(new_val - new_max);
return vec2<f32>(new_max, new_sum);
}
/// Fast approximate exp for softmax (when precision is less critical)
fn fast_exp(x: f32) -> f32 {
// Use native exp for now; can be replaced with polynomial approximation
return exp(x);
}
/// Clamp value to valid range
fn clamp_f32(val: f32, min_val: f32, max_val: f32) -> f32 {
return max(min_val, min(max_val, val));
}

36
crates/prime-radiant/src/lib.rs
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@ -223,6 +223,16 @@ pub mod distributed;
#[cfg_attr(docsrs, doc(cfg(feature = "ruvllm")))]
pub mod ruvllm_integration;
/// GPU acceleration - wgpu-based parallel coherence computation
#[cfg(feature = "gpu")]
#[cfg_attr(docsrs, doc(cfg(feature = "gpu")))]
pub mod gpu;
/// SIMD optimizations - explicit SIMD intrinsics for high-performance computation
#[cfg(feature = "simd")]
#[cfg_attr(docsrs, doc(cfg(feature = "simd")))]
pub mod simd;
// -----------------------------------------------------------------------------
// Shared Types and Errors
// -----------------------------------------------------------------------------
@ -345,6 +355,32 @@ pub use ruvllm_integration::{
CoherenceConfidence, ConfidenceLevel, ConfidenceScore, EnergyContributor,
};
#[cfg(feature = "gpu")]
pub use gpu::{
// Device management
GpuDevice, GpuDeviceInfo, GpuDeviceOptions,
// Buffer management
GpuBuffer, GpuBufferManager, GpuBufferPool, BufferUsage, BufferUsageFlags, BufferKey,
// Pipeline management
ComputePipeline, PipelineCache, BindingDesc, BindingType,
// Dispatch and synchronization
GpuDispatcher, DispatchConfig, DispatchBuilder,
// GPU coherence engine
GpuCoherenceEngine, GpuConfig, GpuCapabilities, GpuCoherenceEnergy,
// Kernel types
ComputeResidualsKernel, ComputeEnergyKernel, SheafAttentionKernel, TokenRoutingKernel,
// Errors
GpuError, GpuResult,
};
#[cfg(feature = "simd")]
pub use simd::{
SimdWidth, SimdContext, best_simd_width,
dot_product_simd, norm_squared_simd, subtract_simd, scale_simd,
matmul_simd, matvec_simd,
batch_residuals_simd, weighted_energy_sum_simd, batch_lane_assignment_simd,
};
// ============================================================================
// PRELUDE MODULE
// ============================================================================

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@ -0,0 +1,696 @@
//! # SIMD Energy Computation
//!
//! High-performance coherence energy computation using SIMD intrinsics.
//! These operations are critical for the hot path of coherence evaluation.
//!
//! ## Key Operations
//!
//! | Operation | Description | Use Case |
//! |-----------|-------------|----------|
//! | `batch_residuals_simd` | Compute residuals for multiple edges | Bulk energy update |
//! | `batch_residual_norms_simd` | Compute squared norms of residuals | Energy aggregation |
//! | `weighted_energy_sum_simd` | Sum residual energies with weights | Total energy |
//! | `batch_lane_assignment_simd` | Branchless lane routing | Gate evaluation |
//!
//! ## Performance Characteristics
//!
//! The batch operations are designed to process multiple edges in parallel,
//! achieving near-optimal memory bandwidth utilization when vector dimensions
//! align with SIMD register widths.
use wide::{f32x8, CmpGe};
use crate::execution::ComputeLane;
/// Compute residuals for multiple edges in parallel.
///
/// Given flattened source and target state vectors, computes the residual
/// for each edge: `residual[i] = source[i] - target[i]`
///
/// # Arguments
///
/// * `sources` - Flattened source states: `[s0_0, s0_1, ..., s1_0, s1_1, ...]`
/// * `targets` - Flattened target states: `[t0_0, t0_1, ..., t1_0, t1_1, ...]`
/// * `residuals` - Output buffer for residuals (same layout as inputs)
/// * `dim` - Dimension of each state vector
/// * `count` - Number of edges to process
///
/// # Layout
///
/// For `count` edges with `dim`-dimensional states:
/// - Total elements = `count * dim`
/// - Edge `i` starts at index `i * dim`
///
/// # Panics
///
/// Panics in debug mode if buffer sizes don't match `dim * count`.
#[inline]
pub fn batch_residuals_simd(
sources: &[f32],
targets: &[f32],
residuals: &mut [f32],
dim: usize,
count: usize,
) {
let total = dim * count;
debug_assert_eq!(sources.len(), total);
debug_assert_eq!(targets.len(), total);
debug_assert_eq!(residuals.len(), total);
// For small batches, use scalar
if total < 32 {
batch_residuals_scalar(sources, targets, residuals);
return;
}
// SIMD subtraction
let chunks_s = sources.chunks_exact(8);
let chunks_t = targets.chunks_exact(8);
let chunks_r = residuals.chunks_exact_mut(8);
let remainder_s = chunks_s.remainder();
let remainder_t = chunks_t.remainder();
let offset = total - remainder_s.len();
for ((cs, ct), cr) in chunks_s.zip(chunks_t).zip(chunks_r) {
let vs = load_f32x8(cs);
let vt = load_f32x8(ct);
let result = vs - vt;
store_f32x8(cr, result);
}
// Handle remainder
for (i, (&vs, &vt)) in remainder_s.iter().zip(remainder_t.iter()).enumerate() {
residuals[offset + i] = vs - vt;
}
}
/// Compute squared norms of residuals for multiple edges.
///
/// This operation computes `||residual_i||^2` for each edge without
/// storing the full residual vectors.
///
/// # Arguments
///
/// * `sources` - Flattened source states
/// * `targets` - Flattened target states
/// * `norms` - Output buffer for squared norms (length = `count`)
/// * `dim` - Dimension of each state vector
/// * `count` - Number of edges
///
/// # Example
///
/// ```rust,ignore
/// use prime_radiant::simd::energy::batch_residual_norms_simd;
///
/// let sources = [1.0, 0.0, 0.0, 0.0]; // 2 edges, dim=2
/// let targets = [0.0, 0.0, 1.0, 0.0];
/// let mut norms = [0.0f32; 2];
///
/// batch_residual_norms_simd(&sources, &targets, &mut norms, 2, 2);
/// // norms[0] = 1.0 (||[1,0] - [0,0]||^2)
/// // norms[1] = 1.0 (||[0,0] - [1,0]||^2)
/// ```
#[inline]
pub fn batch_residual_norms_simd(
sources: &[f32],
targets: &[f32],
norms: &mut [f32],
dim: usize,
count: usize,
) {
debug_assert_eq!(sources.len(), dim * count);
debug_assert_eq!(targets.len(), dim * count);
debug_assert_eq!(norms.len(), count);
// For small dimensions, process edges directly
if dim < 16 {
for i in 0..count {
let offset = i * dim;
norms[i] = compute_residual_norm_sq_scalar(
&sources[offset..offset + dim],
&targets[offset..offset + dim],
);
}
return;
}
// For larger dimensions, use SIMD per-edge
for i in 0..count {
let offset = i * dim;
norms[i] = compute_residual_norm_sq_simd(
&sources[offset..offset + dim],
&targets[offset..offset + dim],
);
}
}
/// Compute residual norm squared for a single edge using SIMD.
///
/// # Arguments
///
/// * `source` - Source state vector
/// * `target` - Target state vector
///
/// # Returns
///
/// `||source - target||^2`
#[inline]
pub fn compute_residual_norm_sq_simd(source: &[f32], target: &[f32]) -> f32 {
debug_assert_eq!(source.len(), target.len());
let len = source.len();
if len < 16 {
return compute_residual_norm_sq_scalar(source, target);
}
let chunks_s = source.chunks_exact(8);
let chunks_t = target.chunks_exact(8);
let remainder_s = chunks_s.remainder();
let remainder_t = chunks_t.remainder();
let mut acc0 = f32x8::ZERO;
let mut acc1 = f32x8::ZERO;
let mut chunks_s_iter = chunks_s;
let mut chunks_t_iter = chunks_t;
// Unroll 2x
while let (Some(cs0), Some(ct0)) = (chunks_s_iter.next(), chunks_t_iter.next()) {
let vs0 = load_f32x8(cs0);
let vt0 = load_f32x8(ct0);
let diff0 = vs0 - vt0;
acc0 = diff0.mul_add(diff0, acc0);
if let (Some(cs1), Some(ct1)) = (chunks_s_iter.next(), chunks_t_iter.next()) {
let vs1 = load_f32x8(cs1);
let vt1 = load_f32x8(ct1);
let diff1 = vs1 - vt1;
acc1 = diff1.mul_add(diff1, acc1);
}
}
let combined = acc0 + acc1;
let mut sum = combined.reduce_add();
// Handle remainder
for (&vs, &vt) in remainder_s.iter().zip(remainder_t.iter()) {
let diff = vs - vt;
sum += diff * diff;
}
sum
}
/// Compute weighted energy sum using SIMD horizontal reduction.
///
/// # Arguments
///
/// * `residual_norms` - Squared norms of residuals: `||r_e||^2`
/// * `weights` - Edge weights: `w_e`
///
/// # Returns
///
/// Total energy: `E(S) = sum(w_e * ||r_e||^2)`
///
/// # Example
///
/// ```rust,ignore
/// use prime_radiant::simd::energy::weighted_energy_sum_simd;
///
/// let norms = [1.0, 4.0, 9.0, 16.0];
/// let weights = [1.0, 0.5, 0.25, 0.125];
/// let energy = weighted_energy_sum_simd(&norms, &weights);
/// // energy = 1*1 + 0.5*4 + 0.25*9 + 0.125*16 = 1 + 2 + 2.25 + 2 = 7.25
/// ```
#[inline]
pub fn weighted_energy_sum_simd(residual_norms: &[f32], weights: &[f32]) -> f32 {
debug_assert_eq!(residual_norms.len(), weights.len());
let len = residual_norms.len();
if len < 16 {
return weighted_energy_sum_scalar(residual_norms, weights);
}
let chunks_n = residual_norms.chunks_exact(8);
let chunks_w = weights.chunks_exact(8);
let remainder_n = chunks_n.remainder();
let remainder_w = chunks_w.remainder();
let mut acc0 = f32x8::ZERO;
let mut acc1 = f32x8::ZERO;
let mut chunks_n_iter = chunks_n;
let mut chunks_w_iter = chunks_w;
// Unroll 2x
while let (Some(cn0), Some(cw0)) = (chunks_n_iter.next(), chunks_w_iter.next()) {
let vn0 = load_f32x8(cn0);
let vw0 = load_f32x8(cw0);
acc0 = vn0.mul_add(vw0, acc0);
if let (Some(cn1), Some(cw1)) = (chunks_n_iter.next(), chunks_w_iter.next()) {
let vn1 = load_f32x8(cn1);
let vw1 = load_f32x8(cw1);
acc1 = vn1.mul_add(vw1, acc1);
}
}
let combined = acc0 + acc1;
let mut sum = combined.reduce_add();
// Handle remainder
for (&n, &w) in remainder_n.iter().zip(remainder_w.iter()) {
sum += n * w;
}
sum
}
/// Batch lane assignment using branchless SIMD comparison.
///
/// Assigns each energy value to a compute lane based on threshold comparison.
/// Uses branchless operations for consistent performance regardless of data.
///
/// # Arguments
///
/// * `energies` - Array of energy values to route
/// * `thresholds` - `[reflex, retrieval, heavy, human]` thresholds
/// * `lanes` - Output buffer for lane assignments (as `u8`)
///
/// # Lane Assignment Logic
///
/// - `energy < reflex` -> Lane 0 (Reflex)
/// - `reflex <= energy < retrieval` -> Lane 1 (Retrieval)
/// - `retrieval <= energy < heavy` -> Lane 2 (Heavy)
/// - `energy >= heavy` -> Lane 3 (Human)
///
/// # Example
///
/// ```rust,ignore
/// use prime_radiant::simd::energy::batch_lane_assignment_simd;
///
/// let energies = [0.1, 0.25, 0.6, 0.9];
/// let thresholds = [0.2, 0.5, 0.8, 1.0];
/// let mut lanes = [0u8; 4];
///
/// batch_lane_assignment_simd(&energies, thresholds, &mut lanes);
/// // lanes = [0, 1, 2, 3] (Reflex, Retrieval, Heavy, Human)
/// ```
#[inline]
pub fn batch_lane_assignment_simd(
energies: &[f32],
thresholds: [f32; 4],
lanes: &mut [u8],
) {
debug_assert_eq!(energies.len(), lanes.len());
let len = energies.len();
// Thresholds for lane boundaries
let t_reflex = thresholds[0];
let t_retrieval = thresholds[1];
let t_heavy = thresholds[2];
if len < 16 {
batch_lane_assignment_scalar(energies, thresholds, lanes);
return;
}
// SIMD thresholds
let vt_reflex = f32x8::splat(t_reflex);
let vt_retrieval = f32x8::splat(t_retrieval);
let vt_heavy = f32x8::splat(t_heavy);
let chunks_e = energies.chunks_exact(8);
let chunks_l = lanes.chunks_exact_mut(8);
let remainder_e = chunks_e.remainder();
let offset = len - remainder_e.len();
for (ce, cl) in chunks_e.zip(chunks_l) {
let ve = load_f32x8(ce);
// Branchless comparison: count thresholds exceeded
// Using cmp_ge which returns a mask, then convert to 0/1
let above_reflex = ve.cmp_ge(vt_reflex);
let above_retrieval = ve.cmp_ge(vt_retrieval);
let above_heavy = ve.cmp_ge(vt_heavy);
// Convert masks to lane indices
// Each comparison adds 1 when true
let arr_e: [f32; 8] = ve.into();
for i in 0..8 {
let e = arr_e[i];
let lane = (e >= t_reflex) as u8
+ (e >= t_retrieval) as u8
+ (e >= t_heavy) as u8;
cl[i] = lane.min(3);
}
}
// Handle remainder
for (i, &e) in remainder_e.iter().enumerate() {
let lane = (e >= t_reflex) as u8
+ (e >= t_retrieval) as u8
+ (e >= t_heavy) as u8;
lanes[offset + i] = lane.min(3);
}
}
/// Convert lane assignments to ComputeLane enum values.
///
/// # Arguments
///
/// * `lane_bytes` - Raw lane assignments (0-3)
///
/// # Returns
///
/// Vector of `ComputeLane` values
pub fn lanes_to_enum(lane_bytes: &[u8]) -> Vec<ComputeLane> {
lane_bytes
.iter()
.map(|&b| ComputeLane::from_u8(b).unwrap_or(ComputeLane::Human))
.collect()
}
/// Compute total energy for a graph with batched operations.
///
/// This is the main entry point for efficient energy computation.
///
/// # Arguments
///
/// * `sources` - Flattened source states
/// * `targets` - Flattened target states
/// * `weights` - Edge weights
/// * `dim` - State vector dimension
/// * `count` - Number of edges
///
/// # Returns
///
/// Total coherence energy: `E(S) = sum(w_e * ||r_e||^2)`
#[inline]
pub fn compute_total_energy_simd(
sources: &[f32],
targets: &[f32],
weights: &[f32],
dim: usize,
count: usize,
) -> f32 {
debug_assert_eq!(sources.len(), dim * count);
debug_assert_eq!(targets.len(), dim * count);
debug_assert_eq!(weights.len(), count);
// Compute residual norms
let mut norms = vec![0.0f32; count];
batch_residual_norms_simd(sources, targets, &mut norms, dim, count);
// Compute weighted sum
weighted_energy_sum_simd(&norms, weights)
}
/// Compute per-edge energies for a graph.
///
/// # Arguments
///
/// * `sources` - Flattened source states
/// * `targets` - Flattened target states
/// * `weights` - Edge weights
/// * `energies` - Output buffer for per-edge energies
/// * `dim` - State vector dimension
/// * `count` - Number of edges
#[inline]
pub fn compute_edge_energies_simd(
sources: &[f32],
targets: &[f32],
weights: &[f32],
energies: &mut [f32],
dim: usize,
count: usize,
) {
debug_assert_eq!(sources.len(), dim * count);
debug_assert_eq!(targets.len(), dim * count);
debug_assert_eq!(weights.len(), count);
debug_assert_eq!(energies.len(), count);
// Compute residual norms
batch_residual_norms_simd(sources, targets, energies, dim, count);
// Multiply by weights in-place
if count < 16 {
for i in 0..count {
energies[i] *= weights[i];
}
return;
}
let chunks_e = energies.chunks_exact_mut(8);
let chunks_w = weights.chunks_exact(8);
let remainder_w = chunks_w.remainder();
let offset = count - remainder_w.len();
for (ce, cw) in chunks_e.zip(chunks_w) {
let ve = load_f32x8(ce);
let vw = load_f32x8(cw);
let result = ve * vw;
store_f32x8(ce, result);
}
for (i, &w) in remainder_w.iter().enumerate() {
energies[offset + i] *= w;
}
}
// ============================================================================
// Scalar Fallback Implementations
// ============================================================================
#[inline(always)]
fn batch_residuals_scalar(sources: &[f32], targets: &[f32], residuals: &mut [f32]) {
for ((s, t), r) in sources.iter().zip(targets.iter()).zip(residuals.iter_mut()) {
*r = s - t;
}
}
#[inline(always)]
fn compute_residual_norm_sq_scalar(source: &[f32], target: &[f32]) -> f32 {
let mut sum = 0.0f32;
for (&s, &t) in source.iter().zip(target.iter()) {
let diff = s - t;
sum += diff * diff;
}
sum
}
#[inline(always)]
fn weighted_energy_sum_scalar(norms: &[f32], weights: &[f32]) -> f32 {
let mut sum = 0.0f32;
for (&n, &w) in norms.iter().zip(weights.iter()) {
sum += n * w;
}
sum
}
#[inline(always)]
fn batch_lane_assignment_scalar(energies: &[f32], thresholds: [f32; 4], lanes: &mut [u8]) {
let t_reflex = thresholds[0];
let t_retrieval = thresholds[1];
let t_heavy = thresholds[2];
for (e, l) in energies.iter().zip(lanes.iter_mut()) {
let lane = (*e >= t_reflex) as u8
+ (*e >= t_retrieval) as u8
+ (*e >= t_heavy) as u8;
*l = lane.min(3);
}
}
// ============================================================================
// Helper Functions
// ============================================================================
#[inline(always)]
fn load_f32x8(slice: &[f32]) -> f32x8 {
debug_assert!(slice.len() >= 8);
let arr: [f32; 8] = [
slice[0], slice[1], slice[2], slice[3],
slice[4], slice[5], slice[6], slice[7],
];
f32x8::from(arr)
}
#[inline(always)]
fn store_f32x8(slice: &mut [f32], v: f32x8) {
debug_assert!(slice.len() >= 8);
let arr: [f32; 8] = v.into();
slice[..8].copy_from_slice(&arr);
}
#[cfg(test)]
mod tests {
use super::*;
const EPSILON: f32 = 1e-4;
fn approx_eq(a: f32, b: f32) -> bool {
// Use relative error for larger values
let max_abs = a.abs().max(b.abs());
if max_abs > 1.0 {
(a - b).abs() / max_abs < EPSILON
} else {
(a - b).abs() < EPSILON
}
}
#[test]
fn test_batch_residuals_small() {
let sources = [1.0, 2.0, 3.0, 4.0];
let targets = [0.5, 1.5, 2.5, 3.5];
let mut residuals = [0.0f32; 4];
batch_residuals_simd(&sources, &targets, &mut residuals, 2, 2);
let expected = [0.5, 0.5, 0.5, 0.5];
for (i, (&r, &e)) in residuals.iter().zip(expected.iter()).enumerate() {
assert!(approx_eq(r, e), "at {} got {} expected {}", i, r, e);
}
}
#[test]
fn test_batch_residuals_large() {
let n = 1024;
let sources: Vec<f32> = (0..n).map(|i| i as f32).collect();
let targets: Vec<f32> = (0..n).map(|i| i as f32 * 0.5).collect();
let mut residuals_simd = vec![0.0f32; n];
let mut residuals_scalar = vec![0.0f32; n];
batch_residuals_simd(&sources, &targets, &mut residuals_simd, 64, 16);
batch_residuals_scalar(&sources, &targets, &mut residuals_scalar);
for (i, (&s, &sc)) in residuals_simd.iter().zip(residuals_scalar.iter()).enumerate() {
assert!(approx_eq(s, sc), "at {} got {} expected {}", i, s, sc);
}
}
#[test]
fn test_batch_residual_norms() {
// 2 edges, dim=2
let sources = [1.0, 0.0, 0.0, 1.0];
let targets = [0.0, 0.0, 1.0, 0.0];
let mut norms = [0.0f32; 2];
batch_residual_norms_simd(&sources, &targets, &mut norms, 2, 2);
// Edge 0: ||(1,0) - (0,0)||^2 = 1
// Edge 1: ||(0,1) - (1,0)||^2 = 1 + 1 = 2
assert!(approx_eq(norms[0], 1.0), "got {}", norms[0]);
assert!(approx_eq(norms[1], 2.0), "got {}", norms[1]);
}
#[test]
fn test_weighted_energy_sum() {
let norms = [1.0, 4.0, 9.0, 16.0];
let weights = [1.0, 0.5, 0.25, 0.125];
let result = weighted_energy_sum_simd(&norms, &weights);
// 1*1 + 0.5*4 + 0.25*9 + 0.125*16 = 1 + 2 + 2.25 + 2 = 7.25
assert!(approx_eq(result, 7.25), "got {}", result);
}
#[test]
fn test_weighted_energy_sum_large() {
let n = 1024;
let norms: Vec<f32> = (0..n).map(|i| i as f32).collect();
let weights: Vec<f32> = (0..n).map(|_| 0.5).collect();
let result = weighted_energy_sum_simd(&norms, &weights);
let expected = weighted_energy_sum_scalar(&norms, &weights);
assert!(approx_eq(result, expected), "got {} expected {}", result, expected);
}
#[test]
fn test_batch_lane_assignment() {
let energies = [0.1, 0.25, 0.6, 0.9];
let thresholds = [0.2, 0.5, 0.8, 1.0];
let mut lanes = [0u8; 4];
batch_lane_assignment_simd(&energies, thresholds, &mut lanes);
// 0.1 < 0.2 -> Lane 0
// 0.2 <= 0.25 < 0.5 -> Lane 1
// 0.5 <= 0.6 < 0.8 -> Lane 2
// 0.8 <= 0.9 < 1.0 -> Lane 3
assert_eq!(lanes, [0, 1, 2, 3]);
}
#[test]
fn test_batch_lane_assignment_large() {
let n = 1024;
let energies: Vec<f32> = (0..n).map(|i| (i as f32) / (n as f32)).collect();
let thresholds = [0.2, 0.5, 0.8, 1.0];
let mut lanes_simd = vec![0u8; n];
let mut lanes_scalar = vec![0u8; n];
batch_lane_assignment_simd(&energies, thresholds, &mut lanes_simd);
batch_lane_assignment_scalar(&energies, thresholds, &mut lanes_scalar);
assert_eq!(lanes_simd, lanes_scalar);
}
#[test]
fn test_compute_total_energy() {
// 2 edges, dim=2
let sources = [1.0, 0.0, 0.0, 1.0];
let targets = [0.0, 0.0, 1.0, 0.0];
let weights = [1.0, 2.0];
let energy = compute_total_energy_simd(&sources, &targets, &weights, 2, 2);
// Edge 0: w=1, ||r||^2 = 1 -> energy = 1
// Edge 1: w=2, ||r||^2 = 2 -> energy = 4
// Total = 5
assert!(approx_eq(energy, 5.0), "got {}", energy);
}
#[test]
fn test_compute_edge_energies() {
let sources = [1.0, 0.0, 0.0, 1.0];
let targets = [0.0, 0.0, 1.0, 0.0];
let weights = [1.0, 2.0];
let mut energies = [0.0f32; 2];
compute_edge_energies_simd(&sources, &targets, &weights, &mut energies, 2, 2);
assert!(approx_eq(energies[0], 1.0), "got {}", energies[0]);
assert!(approx_eq(energies[1], 4.0), "got {}", energies[1]);
}
#[test]
fn test_lanes_to_enum() {
let bytes = [0u8, 1, 2, 3, 0];
let lanes = lanes_to_enum(&bytes);
assert_eq!(lanes[0], ComputeLane::Reflex);
assert_eq!(lanes[1], ComputeLane::Retrieval);
assert_eq!(lanes[2], ComputeLane::Heavy);
assert_eq!(lanes[3], ComputeLane::Human);
assert_eq!(lanes[4], ComputeLane::Reflex);
}
#[test]
fn test_residual_norm_consistency() {
// Verify SIMD and scalar produce same results
let n = 128;
let source: Vec<f32> = (0..n).map(|i| (i as f32) * 0.1).collect();
let target: Vec<f32> = (0..n).map(|i| (i as f32) * 0.2).collect();
let simd_result = compute_residual_norm_sq_simd(&source, &target);
let scalar_result = compute_residual_norm_sq_scalar(&source, &target);
assert!(approx_eq(simd_result, scalar_result),
"simd={} scalar={}", simd_result, scalar_result);
}
}

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//! # SIMD Matrix Operations
//!
//! High-performance matrix operations using SIMD intrinsics.
//! Optimized for small to medium matrices common in coherence computation.
//!
//! ## Matrix Layout
//!
//! All matrices are stored in **row-major** order:
//! - `A[i][j]` is at index `i * cols + j`
//! - This matches Rust's natural 2D array layout
//!
//! ## Supported Operations
//!
//! | Operation | Description | Complexity |
//! |-----------|-------------|------------|
//! | `matmul_simd` | Matrix-matrix multiplication | O(m*k*n) |
//! | `matvec_simd` | Matrix-vector multiplication | O(m*n) |
//! | `transpose_simd` | Matrix transpose | O(m*n) |
//!
//! ## Performance Notes
//!
//! - Uses blocking/tiling for cache-friendly access patterns
//! - Prefetches data for next iteration where beneficial
//! - Falls back to highly optimized scalar code for small matrices
use wide::f32x8;
/// Block size for tiled matrix operations (cache optimization).
const BLOCK_SIZE: usize = 64;
/// Compute matrix-matrix multiplication: C = A * B
///
/// # Arguments
///
/// * `a` - First matrix (m x k), row-major, length = m * k
/// * `b` - Second matrix (k x n), row-major, length = k * n
/// * `c` - Output matrix (m x n), row-major, length = m * n
/// * `m` - Number of rows in A
/// * `k` - Number of columns in A (= rows in B)
/// * `n` - Number of columns in B
///
/// # Panics
///
/// Panics in debug mode if buffer sizes don't match dimensions.
///
/// # Example
///
/// ```rust,ignore
/// use prime_radiant::simd::matrix::matmul_simd;
///
/// // 2x3 * 3x2 = 2x2
/// let a = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]; // 2x3
/// let b = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]; // 3x2
/// let mut c = [0.0f32; 4]; // 2x2
///
/// matmul_simd(&a, &b, &mut c, 2, 3, 2);
/// // c = [22, 28, 49, 64]
/// ```
#[inline]
pub fn matmul_simd(a: &[f32], b: &[f32], c: &mut [f32], m: usize, k: usize, n: usize) {
debug_assert_eq!(a.len(), m * k, "Matrix A size mismatch");
debug_assert_eq!(b.len(), k * n, "Matrix B size mismatch");
debug_assert_eq!(c.len(), m * n, "Matrix C size mismatch");
// Clear output
c.fill(0.0);
// For small matrices, use simple implementation
if m * n < 256 || k < 8 {
matmul_scalar(a, b, c, m, k, n);
return;
}
// Blocked/tiled multiplication for cache efficiency
matmul_blocked(a, b, c, m, k, n);
}
/// Compute matrix-vector multiplication: y = A * x
///
/// # Arguments
///
/// * `a` - Matrix (m x n), row-major
/// * `x` - Input vector (length n)
/// * `y` - Output vector (length m)
/// * `m` - Number of rows
/// * `n` - Number of columns
///
/// # Panics
///
/// Panics in debug mode if buffer sizes don't match dimensions.
#[inline]
pub fn matvec_simd(a: &[f32], x: &[f32], y: &mut [f32], m: usize, n: usize) {
debug_assert_eq!(a.len(), m * n, "Matrix A size mismatch");
debug_assert_eq!(x.len(), n, "Vector x size mismatch");
debug_assert_eq!(y.len(), m, "Vector y size mismatch");
// For small matrices, use scalar implementation
if n < 16 {
matvec_scalar(a, x, y, m, n);
return;
}
// Process each row
for i in 0..m {
let row_start = i * n;
let row = &a[row_start..row_start + n];
y[i] = dot_product_simd(row, x);
}
}
/// Transpose a matrix: B = A^T
///
/// # Arguments
///
/// * `a` - Input matrix (m x n), row-major
/// * `b` - Output matrix (n x m), row-major
/// * `m` - Number of rows in A
/// * `n` - Number of columns in A
#[inline]
pub fn transpose_simd(a: &[f32], b: &mut [f32], m: usize, n: usize) {
debug_assert_eq!(a.len(), m * n);
debug_assert_eq!(b.len(), m * n);
// For small matrices, use scalar transpose
if m < 8 || n < 8 {
transpose_scalar(a, b, m, n);
return;
}
// Block-based transpose for cache efficiency
let block = 8;
for ii in (0..m).step_by(block) {
for jj in (0..n).step_by(block) {
// Process block
let i_end = (ii + block).min(m);
let j_end = (jj + block).min(n);
for i in ii..i_end {
for j in jj..j_end {
b[j * m + i] = a[i * n + j];
}
}
}
}
}
/// Compute outer product: C = a * b^T
///
/// # Arguments
///
/// * `a` - Column vector (length m)
/// * `b` - Row vector (length n)
/// * `c` - Output matrix (m x n), row-major
#[inline]
pub fn outer_product_simd(a: &[f32], b: &[f32], c: &mut [f32]) {
let m = a.len();
let n = b.len();
debug_assert_eq!(c.len(), m * n);
if n < 16 {
// Scalar fallback
for i in 0..m {
for j in 0..n {
c[i * n + j] = a[i] * b[j];
}
}
return;
}
// SIMD version: each row of C is a[i] * b
for i in 0..m {
let scalar = a[i];
let scalar_vec = f32x8::splat(scalar);
let row_start = i * n;
let chunks_b = b.chunks_exact(8);
let chunks_c = c[row_start..row_start + n].chunks_exact_mut(8);
let remainder_b = chunks_b.remainder();
let offset = n - remainder_b.len();
for (cb, cc) in chunks_b.zip(chunks_c) {
let vb = load_f32x8(cb);
let result = vb * scalar_vec;
store_f32x8(cc, result);
}
// Handle remainder
for (j, &bj) in remainder_b.iter().enumerate() {
c[row_start + offset + j] = scalar * bj;
}
}
}
/// Add two matrices element-wise: C = A + B
#[inline]
pub fn matadd_simd(a: &[f32], b: &[f32], c: &mut [f32]) {
debug_assert_eq!(a.len(), b.len());
debug_assert_eq!(a.len(), c.len());
let n = a.len();
if n < 16 {
for i in 0..n {
c[i] = a[i] + b[i];
}
return;
}
let chunks_a = a.chunks_exact(8);
let chunks_b = b.chunks_exact(8);
let chunks_c = c.chunks_exact_mut(8);
let remainder_a = chunks_a.remainder();
let remainder_b = chunks_b.remainder();
let offset = n - remainder_a.len();
for ((ca, cb), cc) in chunks_a.zip(chunks_b).zip(chunks_c) {
let va = load_f32x8(ca);
let vb = load_f32x8(cb);
let result = va + vb;
store_f32x8(cc, result);
}
for (i, (&va, &vb)) in remainder_a.iter().zip(remainder_b.iter()).enumerate() {
c[offset + i] = va + vb;
}
}
/// Scale a matrix by a scalar: B = alpha * A
#[inline]
pub fn matscale_simd(a: &[f32], alpha: f32, b: &mut [f32]) {
debug_assert_eq!(a.len(), b.len());
let n = a.len();
if n < 16 {
for i in 0..n {
b[i] = alpha * a[i];
}
return;
}
let alpha_vec = f32x8::splat(alpha);
let chunks_a = a.chunks_exact(8);
let chunks_b = b.chunks_exact_mut(8);
let remainder_a = chunks_a.remainder();
let offset = n - remainder_a.len();
for (ca, cb) in chunks_a.zip(chunks_b) {
let va = load_f32x8(ca);
let result = va * alpha_vec;
store_f32x8(cb, result);
}
for (i, &va) in remainder_a.iter().enumerate() {
b[offset + i] = alpha * va;
}
}
// ============================================================================
// Internal Implementations
// ============================================================================
/// Blocked matrix multiplication for cache efficiency.
fn matmul_blocked(a: &[f32], b: &[f32], c: &mut [f32], m: usize, k: usize, n: usize) {
// Use smaller block size for k dimension to keep data in L1 cache
let bk = BLOCK_SIZE.min(k);
let bn = BLOCK_SIZE.min(n);
for kk in (0..k).step_by(bk) {
let k_end = (kk + bk).min(k);
for jj in (0..n).step_by(bn) {
let j_end = (jj + bn).min(n);
for i in 0..m {
let c_row = i * n;
let a_row = i * k;
// Process this block of the output row
for kc in kk..k_end {
let a_val = a[a_row + kc];
let a_vec = f32x8::splat(a_val);
let b_row = kc * n;
// SIMD inner loop
let mut j = jj;
while j + 8 <= j_end {
let b_chunk = &b[b_row + j..b_row + j + 8];
let c_chunk = &mut c[c_row + j..c_row + j + 8];
let vb = load_f32x8(b_chunk);
let vc = load_f32x8(c_chunk);
let result = a_vec.mul_add(vb, vc);
store_f32x8(c_chunk, result);
j += 8;
}
// Scalar cleanup
while j < j_end {
c[c_row + j] += a_val * b[b_row + j];
j += 1;
}
}
}
}
}
}
/// Simple scalar matrix multiplication for small matrices.
fn matmul_scalar(a: &[f32], b: &[f32], c: &mut [f32], m: usize, k: usize, n: usize) {
for i in 0..m {
for j in 0..n {
let mut sum = 0.0f32;
for kc in 0..k {
sum += a[i * k + kc] * b[kc * n + j];
}
c[i * n + j] = sum;
}
}
}
/// Scalar matrix-vector multiplication.
fn matvec_scalar(a: &[f32], x: &[f32], y: &mut [f32], m: usize, n: usize) {
for i in 0..m {
let mut sum = 0.0f32;
let row_start = i * n;
for j in 0..n {
sum += a[row_start + j] * x[j];
}
y[i] = sum;
}
}
/// Scalar matrix transpose.
fn transpose_scalar(a: &[f32], b: &mut [f32], m: usize, n: usize) {
for i in 0..m {
for j in 0..n {
b[j * m + i] = a[i * n + j];
}
}
}
/// SIMD dot product (copied from vectors module to avoid circular dep).
fn dot_product_simd(a: &[f32], b: &[f32]) -> f32 {
let n = a.len();
if n < 16 {
let mut sum = 0.0f32;
for i in 0..n {
sum += a[i] * b[i];
}
return sum;
}
let chunks_a = a.chunks_exact(8);
let chunks_b = b.chunks_exact(8);
let remainder_a = chunks_a.remainder();
let remainder_b = chunks_b.remainder();
let mut acc = f32x8::ZERO;
for (ca, cb) in chunks_a.zip(chunks_b) {
let va = load_f32x8(ca);
let vb = load_f32x8(cb);
acc = va.mul_add(vb, acc);
}
let mut sum = acc.reduce_add();
for (&va, &vb) in remainder_a.iter().zip(remainder_b.iter()) {
sum += va * vb;
}
sum
}
// ============================================================================
// Helper Functions
// ============================================================================
#[inline(always)]
fn load_f32x8(slice: &[f32]) -> f32x8 {
debug_assert!(slice.len() >= 8);
let arr: [f32; 8] = [
slice[0], slice[1], slice[2], slice[3],
slice[4], slice[5], slice[6], slice[7],
];
f32x8::from(arr)
}
#[inline(always)]
fn store_f32x8(slice: &mut [f32], v: f32x8) {
debug_assert!(slice.len() >= 8);
let arr: [f32; 8] = v.into();
slice[..8].copy_from_slice(&arr);
}
#[cfg(test)]
mod tests {
use super::*;
const EPSILON: f32 = 1e-3;
fn approx_eq(a: f32, b: f32) -> bool {
// Use relative error for larger values
let max_abs = a.abs().max(b.abs());
if max_abs > 1.0 {
(a - b).abs() / max_abs < EPSILON
} else {
(a - b).abs() < EPSILON
}
}
fn matrices_approx_eq(a: &[f32], b: &[f32]) -> bool {
a.len() == b.len() && a.iter().zip(b.iter()).all(|(&x, &y)| approx_eq(x, y))
}
#[test]
fn test_matmul_small() {
// 2x3 * 3x2 = 2x2
let a = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]; // 2x3
let b = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]; // 3x2
let mut c = [0.0f32; 4]; // 2x2
matmul_simd(&a, &b, &mut c, 2, 3, 2);
// Row 0: [1,2,3] * [1,3,5; 2,4,6] = [1*1+2*3+3*5, 1*2+2*4+3*6] = [22, 28]
// Row 1: [4,5,6] * [1,3,5; 2,4,6] = [4*1+5*3+6*5, 4*2+5*4+6*6] = [49, 64]
let expected = [22.0, 28.0, 49.0, 64.0];
assert!(matrices_approx_eq(&c, &expected), "got {:?}", c);
}
#[test]
fn test_matmul_identity() {
// I * A = A
let n = 64;
let mut identity = vec![0.0f32; n * n];
for i in 0..n {
identity[i * n + i] = 1.0;
}
let a: Vec<f32> = (0..n * n).map(|i| i as f32).collect();
let mut c = vec![0.0f32; n * n];
matmul_simd(&identity, &a, &mut c, n, n, n);
assert!(matrices_approx_eq(&c, &a));
}
#[test]
fn test_matvec_small() {
// 2x3 matrix * 3-vector
let a = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]; // 2x3
let x = [1.0, 2.0, 3.0]; // 3
let mut y = [0.0f32; 2]; // 2
matvec_simd(&a, &x, &mut y, 2, 3);
// y[0] = 1*1 + 2*2 + 3*3 = 14
// y[1] = 4*1 + 5*2 + 6*3 = 32
let expected = [14.0, 32.0];
assert!(matrices_approx_eq(&y, &expected), "got {:?}", y);
}
#[test]
fn test_matvec_large() {
let m = 64;
let n = 128;
let a: Vec<f32> = (0..m * n).map(|i| (i as f32) * 0.01).collect();
let x: Vec<f32> = (0..n).map(|i| i as f32).collect();
let mut y_simd = vec![0.0f32; m];
let mut y_scalar = vec![0.0f32; m];
matvec_simd(&a, &x, &mut y_simd, m, n);
matvec_scalar(&a, &x, &mut y_scalar, m, n);
assert!(matrices_approx_eq(&y_simd, &y_scalar));
}
#[test]
fn test_transpose_small() {
// 2x3 -> 3x2
let a = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]; // 2x3
let mut b = [0.0f32; 6]; // 3x2
transpose_simd(&a, &mut b, 2, 3);
// Transposed: [[1,4], [2,5], [3,6]]
let expected = [1.0, 4.0, 2.0, 5.0, 3.0, 6.0];
assert_eq!(b, expected);
}
#[test]
fn test_transpose_large() {
let m = 32;
let n = 64;
let a: Vec<f32> = (0..m * n).map(|i| i as f32).collect();
let mut b = vec![0.0f32; m * n];
transpose_simd(&a, &mut b, m, n);
// Verify transpose property
for i in 0..m {
for j in 0..n {
assert!(approx_eq(a[i * n + j], b[j * m + i]),
"mismatch at ({}, {})", i, j);
}
}
}
#[test]
fn test_outer_product() {
let a = [1.0, 2.0, 3.0];
let b = [4.0, 5.0];
let mut c = [0.0f32; 6];
outer_product_simd(&a, &b, &mut c);
// c[i,j] = a[i] * b[j]
let expected = [4.0, 5.0, 8.0, 10.0, 12.0, 15.0];
assert!(matrices_approx_eq(&c, &expected));
}
#[test]
fn test_matadd() {
let a = [1.0, 2.0, 3.0, 4.0];
let b = [5.0, 6.0, 7.0, 8.0];
let mut c = [0.0f32; 4];
matadd_simd(&a, &b, &mut c);
assert_eq!(c, [6.0, 8.0, 10.0, 12.0]);
}
#[test]
fn test_matscale() {
let a = [1.0, 2.0, 3.0, 4.0];
let mut b = [0.0f32; 4];
matscale_simd(&a, 2.5, &mut b);
assert!(matrices_approx_eq(&b, &[2.5, 5.0, 7.5, 10.0]));
}
#[test]
fn test_matmul_large() {
// Test with sizes that exercise the blocked algorithm
let m = 128;
let k = 96;
let n = 64;
let a: Vec<f32> = (0..m * k).map(|i| (i as f32) * 0.001).collect();
let b: Vec<f32> = (0..k * n).map(|i| (i as f32) * 0.001).collect();
let mut c_simd = vec![0.0f32; m * n];
let mut c_scalar = vec![0.0f32; m * n];
matmul_simd(&a, &b, &mut c_simd, m, k, n);
matmul_scalar(&a, &b, &mut c_scalar, m, k, n);
// Allow slightly more tolerance for larger matrices due to accumulation
for i in 0..m * n {
assert!((c_simd[i] - c_scalar[i]).abs() < 0.01,
"mismatch at {}: {} vs {}", i, c_simd[i], c_scalar[i]);
}
}
}

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//! # SIMD Optimizations for Prime-Radiant
//!
//! This module provides explicit SIMD (Single Instruction, Multiple Data) intrinsics
//! for high-performance coherence computation. The implementation supports multiple
//! SIMD widths with automatic runtime detection.
//!
//! ## Architecture Support
//!
//! | Architecture | SIMD Extension | Width | Features |
//! |--------------|----------------|-------|----------|
//! | x86_64 | SSE4.2 | 128-bit | Baseline vector support |
//! | x86_64 | AVX2 | 256-bit | 8x f32 parallel ops |
//! | x86_64 | AVX-512 | 512-bit | 16x f32 parallel ops |
//! | aarch64 | NEON | 128-bit | ARM vector support |
//!
//! ## Implementation Strategy
//!
//! 1. **Primary**: `std::simd` with `portable_simd` feature (nightly)
//! 2. **Fallback**: `wide` crate for stable Rust compatibility
//! 3. **Scalar**: Auto-vectorizable fallback for unsupported platforms
//!
//! ## Performance Targets
//!
//! | Operation | Scalar | SIMD (AVX2) | Speedup |
//! |-----------|--------|-------------|---------|
//! | `dot_product` (1024-dim) | 1.2us | 0.15us | ~8x |
//! | `norm_squared` (1024-dim) | 0.8us | 0.10us | ~8x |
//! | `batch_residuals` (256 edges) | 50us | 6.5us | ~7.7x |
//! | `batch_lane_assignment` (1024) | 4us | 0.5us | ~8x |
//!
//! ## Usage
//!
//! ```rust,ignore
//! use prime_radiant::simd::{SimdWidth, best_simd_width, vectors, energy};
//!
//! // Auto-detect best SIMD width at runtime
//! let width = best_simd_width();
//! println!("Using {:?}", width);
//!
//! // SIMD dot product
//! let a = [1.0f32; 256];
//! let b = [2.0f32; 256];
//! let result = vectors::dot_product_simd(&a, &b);
//! ```
pub mod vectors;
pub mod matrix;
pub mod energy;
// Re-export key types
pub use vectors::{dot_product_simd, norm_squared_simd, subtract_simd, scale_simd};
pub use matrix::{matmul_simd, matvec_simd};
pub use energy::{
batch_residuals_simd, weighted_energy_sum_simd, batch_lane_assignment_simd,
batch_residual_norms_simd,
};
/// Available SIMD instruction set widths.
///
/// The actual width available depends on the CPU and detected features.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
#[repr(u8)]
pub enum SimdWidth {
/// No SIMD available, use scalar operations
Scalar = 0,
/// SSE4.2: 128-bit (4x f32)
Sse42 = 1,
/// AVX2: 256-bit (8x f32)
Avx2 = 2,
/// AVX-512: 512-bit (16x f32)
Avx512 = 3,
/// ARM NEON: 128-bit (4x f32)
Neon = 4,
}
impl SimdWidth {
/// Number of f32 values that can be processed in parallel.
#[inline]
pub const fn lanes_f32(self) -> usize {
match self {
SimdWidth::Scalar => 1,
SimdWidth::Sse42 | SimdWidth::Neon => 4,
SimdWidth::Avx2 => 8,
SimdWidth::Avx512 => 16,
}
}
/// Number of f64 values that can be processed in parallel.
#[inline]
pub const fn lanes_f64(self) -> usize {
match self {
SimdWidth::Scalar => 1,
SimdWidth::Sse42 | SimdWidth::Neon => 2,
SimdWidth::Avx2 => 4,
SimdWidth::Avx512 => 8,
}
}
/// Whether this SIMD width is supported on the current CPU.
#[inline]
pub fn is_supported(self) -> bool {
match self {
SimdWidth::Scalar => true,
SimdWidth::Sse42 => cfg!(target_arch = "x86_64") && is_sse42_supported(),
SimdWidth::Avx2 => cfg!(target_arch = "x86_64") && is_avx2_supported(),
SimdWidth::Avx512 => cfg!(target_arch = "x86_64") && is_avx512_supported(),
SimdWidth::Neon => cfg!(target_arch = "aarch64") && is_neon_supported(),
}
}
/// Get a human-readable name for this SIMD width.
pub const fn name(self) -> &'static str {
match self {
SimdWidth::Scalar => "Scalar",
SimdWidth::Sse42 => "SSE4.2",
SimdWidth::Avx2 => "AVX2",
SimdWidth::Avx512 => "AVX-512",
SimdWidth::Neon => "NEON",
}
}
}
impl Default for SimdWidth {
fn default() -> Self {
best_simd_width()
}
}
/// Detect the best available SIMD width for the current CPU.
///
/// This function performs runtime CPU feature detection and returns
/// the highest-performance SIMD instruction set available.
///
/// # Example
///
/// ```rust,ignore
/// use prime_radiant::simd::best_simd_width;
///
/// let width = best_simd_width();
/// match width {
/// SimdWidth::Avx512 => println!("AVX-512 available!"),
/// SimdWidth::Avx2 => println!("AVX2 available"),
/// _ => println!("Using {:?}", width),
/// }
/// ```
#[inline]
pub fn best_simd_width() -> SimdWidth {
#[cfg(target_arch = "x86_64")]
{
if is_avx512_supported() {
return SimdWidth::Avx512;
}
if is_avx2_supported() {
return SimdWidth::Avx2;
}
if is_sse42_supported() {
return SimdWidth::Sse42;
}
}
#[cfg(target_arch = "aarch64")]
{
if is_neon_supported() {
return SimdWidth::Neon;
}
}
SimdWidth::Scalar
}
/// Check if SSE4.2 is supported (x86_64).
#[cfg(target_arch = "x86_64")]
#[inline]
fn is_sse42_supported() -> bool {
#[cfg(target_feature = "sse4.2")]
{
true
}
#[cfg(not(target_feature = "sse4.2"))]
{
std::arch::is_x86_feature_detected!("sse4.2")
}
}
#[cfg(not(target_arch = "x86_64"))]
#[inline]
fn is_sse42_supported() -> bool {
false
}
/// Check if AVX2 is supported (x86_64).
#[cfg(target_arch = "x86_64")]
#[inline]
fn is_avx2_supported() -> bool {
#[cfg(target_feature = "avx2")]
{
true
}
#[cfg(not(target_feature = "avx2"))]
{
std::arch::is_x86_feature_detected!("avx2")
}
}
#[cfg(not(target_arch = "x86_64"))]
#[inline]
fn is_avx2_supported() -> bool {
false
}
/// Check if AVX-512 is supported (x86_64).
#[cfg(target_arch = "x86_64")]
#[inline]
fn is_avx512_supported() -> bool {
#[cfg(target_feature = "avx512f")]
{
true
}
#[cfg(not(target_feature = "avx512f"))]
{
std::arch::is_x86_feature_detected!("avx512f")
}
}
#[cfg(not(target_arch = "x86_64"))]
#[inline]
fn is_avx512_supported() -> bool {
false
}
/// Check if NEON is supported (aarch64).
#[cfg(target_arch = "aarch64")]
#[inline]
fn is_neon_supported() -> bool {
// NEON is mandatory on aarch64
true
}
#[cfg(not(target_arch = "aarch64"))]
#[inline]
fn is_neon_supported() -> bool {
false
}
/// SIMD runtime context for operation dispatch.
///
/// Caches the detected SIMD width to avoid repeated feature detection.
#[derive(Debug, Clone)]
pub struct SimdContext {
/// The detected SIMD width for this CPU.
pub width: SimdWidth,
/// Number of f32 lanes available.
pub f32_lanes: usize,
/// Number of f64 lanes available.
pub f64_lanes: usize,
}
impl SimdContext {
/// Create a new SIMD context with auto-detection.
pub fn new() -> Self {
let width = best_simd_width();
Self {
width,
f32_lanes: width.lanes_f32(),
f64_lanes: width.lanes_f64(),
}
}
/// Create a context with a specific SIMD width (for testing).
///
/// # Panics
///
/// Panics if the requested width is not supported on this CPU.
pub fn with_width(width: SimdWidth) -> Self {
assert!(width.is_supported(), "SIMD width {:?} not supported", width);
Self {
width,
f32_lanes: width.lanes_f32(),
f64_lanes: width.lanes_f64(),
}
}
/// Get a reference to the global SIMD context.
///
/// This is lazily initialized on first access.
pub fn global() -> &'static SimdContext {
use once_cell::sync::Lazy;
static CONTEXT: Lazy<SimdContext> = Lazy::new(SimdContext::new);
&CONTEXT
}
}
impl Default for SimdContext {
fn default() -> Self {
Self::new()
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_simd_width_detection() {
let width = best_simd_width();
println!("Detected SIMD width: {:?}", width);
assert!(width.is_supported());
}
#[test]
fn test_simd_lanes() {
assert_eq!(SimdWidth::Scalar.lanes_f32(), 1);
assert_eq!(SimdWidth::Sse42.lanes_f32(), 4);
assert_eq!(SimdWidth::Avx2.lanes_f32(), 8);
assert_eq!(SimdWidth::Avx512.lanes_f32(), 16);
assert_eq!(SimdWidth::Neon.lanes_f32(), 4);
}
#[test]
fn test_simd_context() {
let ctx = SimdContext::new();
assert!(ctx.width.is_supported());
assert_eq!(ctx.f32_lanes, ctx.width.lanes_f32());
}
#[test]
fn test_global_context() {
let ctx1 = SimdContext::global();
let ctx2 = SimdContext::global();
assert_eq!(ctx1.width, ctx2.width);
}
}

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//! # SIMD Vector Operations
//!
//! High-performance vector operations using explicit SIMD intrinsics.
//! All operations fall back to optimized scalar code when SIMD is unavailable.
//!
//! ## Supported Operations
//!
//! | Operation | Description | Complexity |
//! |-----------|-------------|------------|
//! | `dot_product_simd` | Inner product of two vectors | O(n) |
//! | `norm_squared_simd` | Squared L2 norm | O(n) |
//! | `subtract_simd` | Element-wise subtraction | O(n) |
//! | `scale_simd` | Scalar multiplication | O(n) |
//!
//! ## Performance Notes
//!
//! - Vectors should be aligned to cache line boundaries for best performance
//! - Processing 8 elements at a time with AVX2 achieves ~8x throughput
//! - Small vectors (<32 elements) may not benefit from SIMD overhead
use wide::f32x8;
/// Compute the dot product of two f32 slices using SIMD.
///
/// # Arguments
///
/// * `a` - First input vector
/// * `b` - Second input vector (must have same length as `a`)
///
/// # Returns
///
/// The dot product: sum(a[i] * b[i])
///
/// # Panics
///
/// Panics in debug mode if vectors have different lengths.
///
/// # Example
///
/// ```rust,ignore
/// use prime_radiant::simd::vectors::dot_product_simd;
///
/// let a = [1.0, 2.0, 3.0, 4.0];
/// let b = [4.0, 3.0, 2.0, 1.0];
/// let result = dot_product_simd(&a, &b);
/// assert!((result - 20.0).abs() < 1e-6);
/// ```
#[inline]
pub fn dot_product_simd(a: &[f32], b: &[f32]) -> f32 {
debug_assert_eq!(a.len(), b.len(), "Vectors must have equal length");
let len = a.len();
// Fast path for small vectors - avoid SIMD overhead
if len < 16 {
return dot_product_scalar(a, b);
}
// Process 8 elements at a time with AVX2/wide
let chunks_a = a.chunks_exact(8);
let chunks_b = b.chunks_exact(8);
let remainder_a = chunks_a.remainder();
let remainder_b = chunks_b.remainder();
// Use 4 accumulators for better ILP (Instruction Level Parallelism)
let mut acc0 = f32x8::ZERO;
let mut acc1 = f32x8::ZERO;
let mut acc2 = f32x8::ZERO;
let mut acc3 = f32x8::ZERO;
let mut chunks_a_iter = chunks_a;
let mut chunks_b_iter = chunks_b;
// Unroll 4x for better throughput
while let (Some(ca0), Some(cb0)) = (chunks_a_iter.next(), chunks_b_iter.next()) {
let va0 = load_f32x8(ca0);
let vb0 = load_f32x8(cb0);
acc0 = va0.mul_add(vb0, acc0);
if let (Some(ca1), Some(cb1)) = (chunks_a_iter.next(), chunks_b_iter.next()) {
let va1 = load_f32x8(ca1);
let vb1 = load_f32x8(cb1);
acc1 = va1.mul_add(vb1, acc1);
if let (Some(ca2), Some(cb2)) = (chunks_a_iter.next(), chunks_b_iter.next()) {
let va2 = load_f32x8(ca2);
let vb2 = load_f32x8(cb2);
acc2 = va2.mul_add(vb2, acc2);
if let (Some(ca3), Some(cb3)) = (chunks_a_iter.next(), chunks_b_iter.next()) {
let va3 = load_f32x8(ca3);
let vb3 = load_f32x8(cb3);
acc3 = va3.mul_add(vb3, acc3);
}
}
}
}
// Combine accumulators
let combined = acc0 + acc1 + acc2 + acc3;
let mut sum = combined.reduce_add();
// Handle remainder
for (&va, &vb) in remainder_a.iter().zip(remainder_b.iter()) {
sum += va * vb;
}
sum
}
/// Compute the squared L2 norm of a vector using SIMD.
///
/// # Arguments
///
/// * `v` - Input vector
///
/// # Returns
///
/// The squared norm: sum(v[i]^2)
///
/// # Example
///
/// ```rust,ignore
/// use prime_radiant::simd::vectors::norm_squared_simd;
///
/// let v = [3.0, 4.0];
/// let result = norm_squared_simd(&v);
/// assert!((result - 25.0).abs() < 1e-6);
/// ```
#[inline]
pub fn norm_squared_simd(v: &[f32]) -> f32 {
let len = v.len();
// Fast path for small vectors
if len < 16 {
return norm_squared_scalar(v);
}
let chunks = v.chunks_exact(8);
let remainder = chunks.remainder();
// Use 4 accumulators for better ILP
let mut acc0 = f32x8::ZERO;
let mut acc1 = f32x8::ZERO;
let mut acc2 = f32x8::ZERO;
let mut acc3 = f32x8::ZERO;
let mut chunks_iter = chunks;
// Unroll 4x
while let Some(c0) = chunks_iter.next() {
let v0 = load_f32x8(c0);
acc0 = v0.mul_add(v0, acc0);
if let Some(c1) = chunks_iter.next() {
let v1 = load_f32x8(c1);
acc1 = v1.mul_add(v1, acc1);
if let Some(c2) = chunks_iter.next() {
let v2 = load_f32x8(c2);
acc2 = v2.mul_add(v2, acc2);
if let Some(c3) = chunks_iter.next() {
let v3 = load_f32x8(c3);
acc3 = v3.mul_add(v3, acc3);
}
}
}
}
// Combine accumulators
let combined = acc0 + acc1 + acc2 + acc3;
let mut sum = combined.reduce_add();
// Handle remainder
for &val in remainder {
sum += val * val;
}
sum
}
/// Subtract two vectors element-wise using SIMD: out = a - b
///
/// # Arguments
///
/// * `a` - Minuend vector
/// * `b` - Subtrahend vector
/// * `out` - Output buffer (must have same length as inputs)
///
/// # Panics
///
/// Panics in debug mode if vectors have different lengths.
#[inline]
pub fn subtract_simd(a: &[f32], b: &[f32], out: &mut [f32]) {
debug_assert_eq!(a.len(), b.len(), "Input vectors must have equal length");
debug_assert_eq!(a.len(), out.len(), "Output must have same length as inputs");
let len = a.len();
// Fast path for small vectors
if len < 16 {
subtract_scalar(a, b, out);
return;
}
let chunks_a = a.chunks_exact(8);
let chunks_b = b.chunks_exact(8);
let chunks_out = out.chunks_exact_mut(8);
let remainder_a = chunks_a.remainder();
let remainder_b = chunks_b.remainder();
let offset = len - remainder_a.len();
for ((ca, cb), cout) in chunks_a.zip(chunks_b).zip(chunks_out) {
let va = load_f32x8(ca);
let vb = load_f32x8(cb);
let result = va - vb;
store_f32x8(cout, result);
}
// Handle remainder
for (i, (&va, &vb)) in remainder_a.iter().zip(remainder_b.iter()).enumerate() {
out[offset + i] = va - vb;
}
}
/// Scale a vector by a scalar using SIMD: out = v * scalar
///
/// # Arguments
///
/// * `v` - Input vector
/// * `scalar` - Scaling factor
/// * `out` - Output buffer (must have same length as input)
///
/// # Panics
///
/// Panics in debug mode if output has different length than input.
#[inline]
pub fn scale_simd(v: &[f32], scalar: f32, out: &mut [f32]) {
debug_assert_eq!(v.len(), out.len(), "Output must have same length as input");
let len = v.len();
// Fast path for small vectors
if len < 16 {
scale_scalar(v, scalar, out);
return;
}
let scalar_vec = f32x8::splat(scalar);
let chunks_v = v.chunks_exact(8);
let chunks_out = out.chunks_exact_mut(8);
let remainder_v = chunks_v.remainder();
let offset = len - remainder_v.len();
for (cv, cout) in chunks_v.zip(chunks_out) {
let vv = load_f32x8(cv);
let result = vv * scalar_vec;
store_f32x8(cout, result);
}
// Handle remainder
for (i, &val) in remainder_v.iter().enumerate() {
out[offset + i] = val * scalar;
}
}
/// Compute element-wise sum of squares of differences: sum((a[i] - b[i])^2)
///
/// This is equivalent to `norm_squared_simd(subtract_simd(a, b))` but more efficient
/// as it avoids the intermediate allocation.
///
/// # Arguments
///
/// * `a` - First input vector
/// * `b` - Second input vector
///
/// # Returns
///
/// The squared distance between the vectors.
#[inline]
pub fn squared_distance_simd(a: &[f32], b: &[f32]) -> f32 {
debug_assert_eq!(a.len(), b.len(), "Vectors must have equal length");
let len = a.len();
// Fast path for small vectors
if len < 16 {
return squared_distance_scalar(a, b);
}
let chunks_a = a.chunks_exact(8);
let chunks_b = b.chunks_exact(8);
let remainder_a = chunks_a.remainder();
let remainder_b = chunks_b.remainder();
let mut acc0 = f32x8::ZERO;
let mut acc1 = f32x8::ZERO;
let mut acc2 = f32x8::ZERO;
let mut acc3 = f32x8::ZERO;
let mut chunks_a_iter = chunks_a;
let mut chunks_b_iter = chunks_b;
while let (Some(ca0), Some(cb0)) = (chunks_a_iter.next(), chunks_b_iter.next()) {
let va0 = load_f32x8(ca0);
let vb0 = load_f32x8(cb0);
let diff0 = va0 - vb0;
acc0 = diff0.mul_add(diff0, acc0);
if let (Some(ca1), Some(cb1)) = (chunks_a_iter.next(), chunks_b_iter.next()) {
let va1 = load_f32x8(ca1);
let vb1 = load_f32x8(cb1);
let diff1 = va1 - vb1;
acc1 = diff1.mul_add(diff1, acc1);
if let (Some(ca2), Some(cb2)) = (chunks_a_iter.next(), chunks_b_iter.next()) {
let va2 = load_f32x8(ca2);
let vb2 = load_f32x8(cb2);
let diff2 = va2 - vb2;
acc2 = diff2.mul_add(diff2, acc2);
if let (Some(ca3), Some(cb3)) = (chunks_a_iter.next(), chunks_b_iter.next()) {
let va3 = load_f32x8(ca3);
let vb3 = load_f32x8(cb3);
let diff3 = va3 - vb3;
acc3 = diff3.mul_add(diff3, acc3);
}
}
}
}
let combined = acc0 + acc1 + acc2 + acc3;
let mut sum = combined.reduce_add();
// Handle remainder
for (&va, &vb) in remainder_a.iter().zip(remainder_b.iter()) {
let diff = va - vb;
sum += diff * diff;
}
sum
}
/// Compute weighted sum: sum(a[i] * weights[i])
///
/// # Arguments
///
/// * `values` - Values to sum
/// * `weights` - Corresponding weights
///
/// # Returns
///
/// The weighted sum.
#[inline]
pub fn weighted_sum_simd(values: &[f32], weights: &[f32]) -> f32 {
// This is just a dot product
dot_product_simd(values, weights)
}
/// Fused multiply-add for vectors: out = a * b + c
///
/// Uses FMA instructions when available for better precision and performance.
#[inline]
pub fn fma_simd(a: &[f32], b: &[f32], c: &[f32], out: &mut [f32]) {
debug_assert_eq!(a.len(), b.len());
debug_assert_eq!(a.len(), c.len());
debug_assert_eq!(a.len(), out.len());
let len = a.len();
if len < 16 {
for i in 0..len {
out[i] = a[i].mul_add(b[i], c[i]);
}
return;
}
let chunks_a = a.chunks_exact(8);
let chunks_b = b.chunks_exact(8);
let chunks_c = c.chunks_exact(8);
let chunks_out = out.chunks_exact_mut(8);
let remainder_a = chunks_a.remainder();
let remainder_b = chunks_b.remainder();
let remainder_c = chunks_c.remainder();
let offset = len - remainder_a.len();
for (((ca, cb), cc), cout) in chunks_a.zip(chunks_b).zip(chunks_c).zip(chunks_out) {
let va = load_f32x8(ca);
let vb = load_f32x8(cb);
let vc = load_f32x8(cc);
let result = va.mul_add(vb, vc);
store_f32x8(cout, result);
}
// Handle remainder
for (i, ((&va, &vb), &vc)) in remainder_a
.iter()
.zip(remainder_b.iter())
.zip(remainder_c.iter())
.enumerate()
{
out[offset + i] = va.mul_add(vb, vc);
}
}
// ============================================================================
// Scalar Fallback Implementations
// ============================================================================
#[inline(always)]
fn dot_product_scalar(a: &[f32], b: &[f32]) -> f32 {
// Use 4 accumulators for ILP even in scalar path
let chunks_a = a.chunks_exact(4);
let chunks_b = b.chunks_exact(4);
let rem_a = chunks_a.remainder();
let rem_b = chunks_b.remainder();
let mut acc0 = 0.0f32;
let mut acc1 = 0.0f32;
let mut acc2 = 0.0f32;
let mut acc3 = 0.0f32;
for (ca, cb) in chunks_a.zip(chunks_b) {
acc0 += ca[0] * cb[0];
acc1 += ca[1] * cb[1];
acc2 += ca[2] * cb[2];
acc3 += ca[3] * cb[3];
}
let mut sum = acc0 + acc1 + acc2 + acc3;
for (&a, &b) in rem_a.iter().zip(rem_b.iter()) {
sum += a * b;
}
sum
}
#[inline(always)]
fn norm_squared_scalar(v: &[f32]) -> f32 {
let chunks = v.chunks_exact(4);
let remainder = chunks.remainder();
let mut acc0 = 0.0f32;
let mut acc1 = 0.0f32;
let mut acc2 = 0.0f32;
let mut acc3 = 0.0f32;
for c in chunks {
acc0 += c[0] * c[0];
acc1 += c[1] * c[1];
acc2 += c[2] * c[2];
acc3 += c[3] * c[3];
}
let mut sum = acc0 + acc1 + acc2 + acc3;
for &x in remainder {
sum += x * x;
}
sum
}
#[inline(always)]
fn subtract_scalar(a: &[f32], b: &[f32], out: &mut [f32]) {
for ((va, vb), vo) in a.iter().zip(b.iter()).zip(out.iter_mut()) {
*vo = va - vb;
}
}
#[inline(always)]
fn scale_scalar(v: &[f32], scalar: f32, out: &mut [f32]) {
for (vi, vo) in v.iter().zip(out.iter_mut()) {
*vo = vi * scalar;
}
}
#[inline(always)]
fn squared_distance_scalar(a: &[f32], b: &[f32]) -> f32 {
let mut sum = 0.0f32;
for (&va, &vb) in a.iter().zip(b.iter()) {
let diff = va - vb;
sum += diff * diff;
}
sum
}
// ============================================================================
// Helper Functions
// ============================================================================
/// Load 8 f32 values into a SIMD register.
#[inline(always)]
fn load_f32x8(slice: &[f32]) -> f32x8 {
debug_assert!(slice.len() >= 8);
// SAFETY: We check length in debug mode
let arr: [f32; 8] = [
slice[0], slice[1], slice[2], slice[3],
slice[4], slice[5], slice[6], slice[7],
];
f32x8::from(arr)
}
/// Store 8 f32 values from a SIMD register to a slice.
#[inline(always)]
fn store_f32x8(slice: &mut [f32], v: f32x8) {
debug_assert!(slice.len() >= 8);
let arr: [f32; 8] = v.into();
slice[..8].copy_from_slice(&arr);
}
#[cfg(test)]
mod tests {
use super::*;
const EPSILON: f32 = 1e-4;
fn approx_eq(a: f32, b: f32) -> bool {
// Use relative error for larger values
let max_abs = a.abs().max(b.abs());
if max_abs > 1.0 {
(a - b).abs() / max_abs < EPSILON
} else {
(a - b).abs() < EPSILON
}
}
#[test]
fn test_dot_product_small() {
let a = [1.0, 2.0, 3.0, 4.0];
let b = [4.0, 3.0, 2.0, 1.0];
let result = dot_product_simd(&a, &b);
assert!(approx_eq(result, 20.0), "got {}", result);
}
#[test]
fn test_dot_product_large() {
let n = 1024;
let a: Vec<f32> = (0..n).map(|i| i as f32).collect();
let b: Vec<f32> = (0..n).map(|i| (n - 1 - i) as f32).collect();
let result = dot_product_simd(&a, &b);
let expected = dot_product_scalar(&a, &b);
assert!(approx_eq(result, expected), "got {} expected {}", result, expected);
}
#[test]
fn test_norm_squared_small() {
let v = [3.0, 4.0];
let result = norm_squared_simd(&v);
assert!(approx_eq(result, 25.0), "got {}", result);
}
#[test]
fn test_norm_squared_large() {
let n = 1024;
let v: Vec<f32> = (0..n).map(|i| i as f32 * 0.01).collect();
let result = norm_squared_simd(&v);
let expected = norm_squared_scalar(&v);
assert!(approx_eq(result, expected), "got {} expected {}", result, expected);
}
#[test]
fn test_subtract_small() {
let a = [5.0, 6.0, 7.0, 8.0];
let b = [1.0, 2.0, 3.0, 4.0];
let mut out = [0.0f32; 4];
subtract_simd(&a, &b, &mut out);
assert_eq!(out, [4.0, 4.0, 4.0, 4.0]);
}
#[test]
fn test_subtract_large() {
let n = 1024;
let a: Vec<f32> = (0..n).map(|i| i as f32).collect();
let b: Vec<f32> = (0..n).map(|i| i as f32 * 0.5).collect();
let mut out = vec![0.0f32; n];
subtract_simd(&a, &b, &mut out);
for i in 0..n {
let expected = a[i] - b[i];
assert!(approx_eq(out[i], expected), "at {} got {} expected {}", i, out[i], expected);
}
}
#[test]
fn test_scale_small() {
let v = [1.0, 2.0, 3.0, 4.0];
let mut out = [0.0f32; 4];
scale_simd(&v, 2.0, &mut out);
assert_eq!(out, [2.0, 4.0, 6.0, 8.0]);
}
#[test]
fn test_scale_large() {
let n = 1024;
let v: Vec<f32> = (0..n).map(|i| i as f32).collect();
let mut out = vec![0.0f32; n];
let scalar = 3.5;
scale_simd(&v, scalar, &mut out);
for i in 0..n {
let expected = v[i] * scalar;
assert!(approx_eq(out[i], expected), "at {} got {} expected {}", i, out[i], expected);
}
}
#[test]
fn test_squared_distance() {
let a = [1.0, 2.0, 3.0];
let b = [4.0, 5.0, 6.0];
let result = squared_distance_simd(&a, &b);
// (4-1)^2 + (5-2)^2 + (6-3)^2 = 9 + 9 + 9 = 27
assert!(approx_eq(result, 27.0), "got {}", result);
}
#[test]
fn test_squared_distance_large() {
let n = 1024;
let a: Vec<f32> = (0..n).map(|i| i as f32 * 0.1).collect();
let b: Vec<f32> = (0..n).map(|i| i as f32 * 0.2).collect();
let result = squared_distance_simd(&a, &b);
let expected = squared_distance_scalar(&a, &b);
assert!(approx_eq(result, expected), "got {} expected {}", result, expected);
}
#[test]
fn test_fma() {
let a = [1.0, 2.0, 3.0, 4.0];
let b = [2.0, 2.0, 2.0, 2.0];
let c = [1.0, 1.0, 1.0, 1.0];
let mut out = [0.0f32; 4];
fma_simd(&a, &b, &c, &mut out);
// a * b + c = [3, 5, 7, 9]
assert_eq!(out, [3.0, 5.0, 7.0, 9.0]);
}
#[test]
fn test_edge_cases() {
// Empty vectors
assert!(approx_eq(dot_product_simd(&[], &[]), 0.0));
assert!(approx_eq(norm_squared_simd(&[]), 0.0));
// Single element
assert!(approx_eq(dot_product_simd(&[3.0], &[4.0]), 12.0));
assert!(approx_eq(norm_squared_simd(&[5.0]), 25.0));
}
}

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crates/prime-radiant/tests/gpu_coherence_tests.rs Normal file
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//! GPU Coherence Engine Tests
//!
//! Comprehensive tests verifying GPU computation results match CPU results
//! within floating-point tolerance. These tests ensure correctness of:
//!
//! - GPU buffer management and data transfer
//! - Parallel residual computation
//! - Energy aggregation with tree reduction
//! - CPU fallback mechanism
//!
//! Run with: cargo test --features gpu
#![cfg(feature = "gpu")]
use prime_radiant::gpu::{
GpuCoherenceEngine, GpuConfig, GpuBuffer, GpuParams, GpuEdge, GpuRestrictionMap,
BufferUsage, GpuBufferManager, GpuResult, GpuError,
};
use prime_radiant::substrate::{
SheafGraph, SheafNode, SheafEdge, SheafNodeBuilder, SheafEdgeBuilder,
NodeId, EdgeId,
};
use std::collections::HashMap;
use uuid::Uuid;
/// Floating point tolerance for GPU vs CPU comparison
const TOLERANCE: f32 = 1e-5;
/// Create a simple test graph with 3 nodes forming a triangle
fn create_triangle_graph() -> SheafGraph {
let graph = SheafGraph::new();
// Create three nodes with states
let node1 = SheafNodeBuilder::new()
.state_from_slice(&[1.0, 0.0, 0.0])
.namespace("test")
.build();
let node2 = SheafNodeBuilder::new()
.state_from_slice(&[0.0, 1.0, 0.0])
.namespace("test")
.build();
let node3 = SheafNodeBuilder::new()
.state_from_slice(&[0.0, 0.0, 1.0])
.namespace("test")
.build();
let id1 = graph.add_node(node1);
let id2 = graph.add_node(node2);
let id3 = graph.add_node(node3);
// Create edges with identity restrictions
let edge12 = SheafEdgeBuilder::new(id1, id2)
.identity_restrictions(3)
.weight(1.0)
.namespace("test")
.build();
let edge23 = SheafEdgeBuilder::new(id2, id3)
.identity_restrictions(3)
.weight(1.0)
.namespace("test")
.build();
let edge31 = SheafEdgeBuilder::new(id3, id1)
.identity_restrictions(3)
.weight(1.0)
.namespace("test")
.build();
graph.add_edge(edge12).unwrap();
graph.add_edge(edge23).unwrap();
graph.add_edge(edge31).unwrap();
graph
}
/// Create a coherent graph where all nodes have identical states
fn create_coherent_graph() -> SheafGraph {
let graph = SheafGraph::new();
// All nodes have the same state
let state = [1.0, 1.0, 1.0];
let node1 = SheafNodeBuilder::new()
.state_from_slice(&state)
.build();
let node2 = SheafNodeBuilder::new()
.state_from_slice(&state)
.build();
let id1 = graph.add_node(node1);
let id2 = graph.add_node(node2);
let edge = SheafEdgeBuilder::new(id1, id2)
.identity_restrictions(3)
.weight(1.0)
.build();
graph.add_edge(edge).unwrap();
graph
}
/// Create a larger graph for performance testing
fn create_large_graph(num_nodes: usize, edges_per_node: usize) -> SheafGraph {
let graph = SheafGraph::new();
let state_dim = 64;
// Create nodes with random states
let mut node_ids = Vec::with_capacity(num_nodes);
for i in 0..num_nodes {
let state: Vec<f32> = (0..state_dim)
.map(|j| ((i * state_dim + j) as f32 * 0.01).sin())
.collect();
let node = SheafNodeBuilder::new()
.state_from_slice(&state)
.build();
node_ids.push(graph.add_node(node));
}
// Create edges
for i in 0..num_nodes {
for j in 1..=edges_per_node {
let target_idx = (i + j) % num_nodes;
if i != target_idx {
let edge = SheafEdgeBuilder::new(node_ids[i], node_ids[target_idx])
.identity_restrictions(state_dim)
.weight(1.0)
.build();
// Ignore duplicate edges
let _ = graph.add_edge(edge);
}
}
}
graph
}
// ============================================================================
// GPU Configuration Tests
// ============================================================================
#[test]
fn test_gpu_config_default() {
let config = GpuConfig::default();
assert!(config.enable_fallback);
assert_eq!(config.beta, 1.0);
assert!(config.threshold_lane0 < config.threshold_lane1);
assert!(config.threshold_lane1 < config.threshold_lane2);
assert!(config.timeout_ms > 0);
}
#[test]
fn test_gpu_config_custom() {
let config = GpuConfig {
enable_fallback: false,
beta: 2.0,
threshold_lane0: 0.05,
threshold_lane1: 0.5,
threshold_lane2: 5.0,
..Default::default()
};
assert!(!config.enable_fallback);
assert_eq!(config.beta, 2.0);
assert_eq!(config.threshold_lane0, 0.05);
}
// ============================================================================
// GPU Buffer Tests
// ============================================================================
#[test]
fn test_gpu_params_alignment() {
// GPU struct alignment is critical for correct computation
assert_eq!(std::mem::size_of::<GpuParams>(), 32);
assert_eq!(std::mem::align_of::<GpuParams>(), 4);
}
#[test]
fn test_gpu_edge_alignment() {
assert_eq!(std::mem::size_of::<GpuEdge>(), 32);
assert_eq!(std::mem::align_of::<GpuEdge>(), 4);
}
#[test]
fn test_gpu_restriction_map_alignment() {
assert_eq!(std::mem::size_of::<GpuRestrictionMap>(), 32);
assert_eq!(std::mem::align_of::<GpuRestrictionMap>(), 4);
}
// ============================================================================
// CPU vs GPU Comparison Tests
// ============================================================================
/// Test that GPU energy matches CPU energy for triangle graph
#[tokio::test]
async fn test_gpu_cpu_energy_match_triangle() {
let graph = create_triangle_graph();
// Compute CPU energy
let cpu_energy = graph.compute_energy();
// Try GPU computation
let config = GpuConfig::default();
match GpuCoherenceEngine::try_new(config).await {
Some(mut engine) => {
engine.upload_graph(&graph).unwrap();
let gpu_energy = engine.compute_energy().await.unwrap();
// Compare total energies
let diff = (cpu_energy.total_energy - gpu_energy.total_energy).abs();
assert!(
diff < TOLERANCE,
"Energy mismatch: CPU={}, GPU={}, diff={}",
cpu_energy.total_energy,
gpu_energy.total_energy,
diff
);
// Verify GPU was actually used
assert!(gpu_energy.used_gpu);
}
None => {
// GPU not available, skip test
eprintln!("GPU not available, skipping GPU comparison test");
}
}
}
/// Test that coherent graph has near-zero energy on GPU
#[tokio::test]
async fn test_gpu_coherent_graph() {
let graph = create_coherent_graph();
// CPU energy should be near zero
let cpu_energy = graph.compute_energy();
assert!(
cpu_energy.total_energy < 1e-10,
"CPU energy for coherent graph should be near zero: {}",
cpu_energy.total_energy
);
// Try GPU computation
let config = GpuConfig::default();
if let Some(mut engine) = GpuCoherenceEngine::try_new(config).await {
engine.upload_graph(&graph).unwrap();
let gpu_energy = engine.compute_energy().await.unwrap();
assert!(
gpu_energy.total_energy < 1e-5,
"GPU energy for coherent graph should be near zero: {}",
gpu_energy.total_energy
);
}
}
/// Test per-edge energy computation
#[tokio::test]
async fn test_gpu_per_edge_energies() {
let graph = create_triangle_graph();
// Compute CPU energy
let cpu_energy = graph.compute_energy();
let config = GpuConfig::default();
if let Some(mut engine) = GpuCoherenceEngine::try_new(config).await {
engine.upload_graph(&graph).unwrap();
let gpu_energy = engine.compute_energy().await.unwrap();
// Same number of edge energies
assert_eq!(
cpu_energy.edge_energies.len(),
gpu_energy.edge_energies.len(),
"Edge count mismatch"
);
// Each edge energy should match (order may differ)
let cpu_sum: f32 = cpu_energy.edge_energies.values().sum();
let gpu_sum: f32 = gpu_energy.edge_energies.iter().sum();
let diff = (cpu_sum - gpu_sum).abs();
assert!(
diff < TOLERANCE,
"Sum of edge energies mismatch: CPU={}, GPU={}, diff={}",
cpu_sum,
gpu_sum,
diff
);
}
}
/// Test with larger graph
#[tokio::test]
async fn test_gpu_large_graph() {
let graph = create_large_graph(100, 5);
let cpu_energy = graph.compute_energy();
let config = GpuConfig::default();
if let Some(mut engine) = GpuCoherenceEngine::try_new(config).await {
engine.upload_graph(&graph).unwrap();
let gpu_energy = engine.compute_energy().await.unwrap();
// Allow slightly larger tolerance for large graphs due to floating point accumulation
let diff = (cpu_energy.total_energy - gpu_energy.total_energy).abs();
let relative_diff = diff / cpu_energy.total_energy.max(1.0);
assert!(
relative_diff < 0.01, // 1% relative error
"Large graph energy mismatch: CPU={}, GPU={}, relative_diff={:.2}%",
cpu_energy.total_energy,
gpu_energy.total_energy,
relative_diff * 100.0
);
}
}
// ============================================================================
// Error Handling Tests
// ============================================================================
#[tokio::test]
async fn test_gpu_empty_graph_error() {
let graph = SheafGraph::new();
let config = GpuConfig::default();
if let Some(mut engine) = GpuCoherenceEngine::try_new(config).await {
let result = engine.upload_graph(&graph);
assert!(result.is_err());
match result {
Err(GpuError::EmptyGraph) => {}
Err(e) => panic!("Expected EmptyGraph error, got: {:?}", e),
Ok(_) => panic!("Expected error for empty graph"),
}
}
}
#[test]
fn test_gpu_error_fallback_detection() {
// Test that certain errors trigger fallback
assert!(GpuError::NoAdapter.should_fallback());
assert!(GpuError::NoDevice("test".into()).should_fallback());
assert!(GpuError::DeviceCreation("test".into()).should_fallback());
assert!(GpuError::AdapterRequest("test".into()).should_fallback());
assert!(GpuError::UnsupportedFeature("test".into()).should_fallback());
// These should not trigger fallback
assert!(!GpuError::Timeout(100).should_fallback());
assert!(!GpuError::EmptyGraph.should_fallback());
assert!(!GpuError::BufferRead("test".into()).should_fallback());
}
#[test]
fn test_gpu_error_recoverable() {
assert!(GpuError::Timeout(100).is_recoverable());
assert!(GpuError::BufferRead("test".into()).is_recoverable());
assert!(GpuError::ExecutionFailed("test".into()).is_recoverable());
assert!(!GpuError::NoAdapter.is_recoverable());
assert!(!GpuError::EmptyGraph.is_recoverable());
}
// ============================================================================
// GPU Capabilities Tests
// ============================================================================
#[tokio::test]
async fn test_gpu_capabilities() {
let config = GpuConfig::default();
if let Some(engine) = GpuCoherenceEngine::try_new(config).await {
let caps = engine.capabilities();
// Should have valid device info
assert!(!caps.device_name.is_empty());
assert!(!caps.backend.is_empty());
// Should have reasonable limits
assert!(caps.max_buffer_size > 0);
assert!(caps.max_workgroup_size > 0);
assert!(caps.max_workgroups[0] > 0);
// Should be marked as supported
assert!(caps.supported);
}
}
// ============================================================================
// Synchronous API Tests
// ============================================================================
#[test]
fn test_sync_api() {
use prime_radiant::gpu::sync;
let config = GpuConfig::default();
if let Some(mut engine) = sync::try_create_engine(config) {
let graph = create_triangle_graph();
engine.upload_graph(&graph).unwrap();
let energy = sync::compute_energy(&mut engine).unwrap();
assert!(energy.total_energy > 0.0);
assert!(energy.used_gpu);
}
}
// ============================================================================
// Resource Management Tests
// ============================================================================
#[tokio::test]
async fn test_gpu_resource_release() {
let config = GpuConfig::default();
if let Some(mut engine) = GpuCoherenceEngine::try_new(config).await {
let graph = create_triangle_graph();
// Upload and compute
engine.upload_graph(&graph).unwrap();
let _ = engine.compute_energy().await.unwrap();
// Release resources
engine.release();
// Re-upload should work
engine.upload_graph(&graph).unwrap();
let energy = engine.compute_energy().await.unwrap();
assert!(energy.total_energy > 0.0);
}
}
#[tokio::test]
async fn test_gpu_multiple_computations() {
let config = GpuConfig::default();
if let Some(mut engine) = GpuCoherenceEngine::try_new(config).await {
let graph = create_triangle_graph();
engine.upload_graph(&graph).unwrap();
// Multiple computations should give consistent results
let energy1 = engine.compute_energy().await.unwrap();
let energy2 = engine.compute_energy().await.unwrap();
let energy3 = engine.compute_energy().await.unwrap();
assert!(
(energy1.total_energy - energy2.total_energy).abs() < TOLERANCE,
"Inconsistent results between computations"
);
assert!(
(energy2.total_energy - energy3.total_energy).abs() < TOLERANCE,
"Inconsistent results between computations"
);
}
}
// ============================================================================
// Performance Tests (disabled by default)
// ============================================================================
#[tokio::test]
#[ignore] // Run with: cargo test --features gpu -- --ignored
async fn test_gpu_performance_1k_nodes() {
let graph = create_large_graph(1000, 10);
let edge_count = graph.edge_count();
let config = GpuConfig::default();
if let Some(mut engine) = GpuCoherenceEngine::try_new(config).await {
engine.upload_graph(&graph).unwrap();
// Warm up
let _ = engine.compute_energy().await.unwrap();
// Benchmark
let start = std::time::Instant::now();
let energy = engine.compute_energy().await.unwrap();
let gpu_time = start.elapsed();
// Compare with CPU
let start = std::time::Instant::now();
let cpu_energy = graph.compute_energy();
let cpu_time = start.elapsed();
println!(
"Performance test ({} edges):",
edge_count
);
println!(" GPU: {}us ({} edges/ms)", energy.compute_time_us, edge_count as u64 * 1000 / energy.compute_time_us.max(1));
println!(" CPU: {}us", cpu_time.as_micros());
println!(" Speedup: {:.2}x", cpu_time.as_micros() as f64 / gpu_time.as_micros() as f64);
// Verify correctness
let diff = (cpu_energy.total_energy - energy.total_energy).abs();
let relative_diff = diff / cpu_energy.total_energy.max(1.0);
assert!(relative_diff < 0.01, "Performance test: energy mismatch");
}
}
#[tokio::test]
#[ignore]
async fn test_gpu_performance_10k_nodes() {
let graph = create_large_graph(10000, 10);
let edge_count = graph.edge_count();
let config = GpuConfig::default();
if let Some(mut engine) = GpuCoherenceEngine::try_new(config).await {
engine.upload_graph(&graph).unwrap();
// Warm up
let _ = engine.compute_energy().await.unwrap();
// Benchmark
let energy = engine.compute_energy().await.unwrap();
println!(
"Large scale test ({} edges): {}us, {} edges/ms",
edge_count,
energy.compute_time_us,
edge_count as u64 * 1000 / energy.compute_time_us.max(1)
);
assert!(energy.total_energy > 0.0);
}
}