trueno 0.16.4

Multi-Target High-Performance Compute Library

trueno (Spanish: "thunder") provides unified compute primitives across CPU SIMD, GPU, and WebAssembly.

Table of Contents

• Features
• Installation
• Quick Start
• Performance
• trueno-gpu: Pure Rust CUDA
• Training (WGPU)
• Operations
• Development
• Contributing
• License

Features

• CPU SIMD: x86 (SSE2/AVX/AVX2/AVX-512), ARM (NEON), WASM (SIMD128)
• GPU: Pure Rust PTX generation via trueno-gpu (no nvcc required)
• Native Blackwell: sm_121 support via PTX 8.8 ISA
• JIT Disk Cache: Compiled PTX cached at ~/.cache/trueno/ptx/ for instant reload
• Cross-platform GPU: Vulkan/Metal/DX12/WebGPU via wgpu
• Auto-dispatch: Runtime selection of optimal backend
• Zero unsafe in public API: Safety via type system

Installation

[dependencies]
trueno = "0.16"

# Optional: GPU support for large matrices
trueno = { version = "0.16", features = ["gpu"] }

# Optional: Pure Rust CUDA PTX generation
trueno-gpu = "0.4"

Quick Start

use trueno::{Vector, Matrix, SymmetricEigen};

// Vector operations - auto-selects best SIMD backend
let a = Vector::from_slice(&[1.0, 2.0, 3.0, 4.0]);
let b = Vector::from_slice(&[5.0, 6.0, 7.0, 8.0]);

let sum = a.add(&b).unwrap();           // [6.0, 8.0, 10.0, 12.0]
let dot = a.dot(&b).unwrap();           // 70.0
let activated = a.relu().unwrap();      // ReLU activation

// Matrix operations
let m = Matrix::from_vec(2, 2, vec![1.0, 2.0, 3.0, 4.0]).unwrap();
let product = m.matmul(&m).unwrap();    // Matrix multiplication
let transposed = m.transpose();          // Transpose

// Batched matmul for transformers (Q @ K^T pattern)
let batch = 2; let heads = 4; let seq = 8; let dim = 64;
let q: Vec<f32> = vec![0.1; batch * heads * seq * dim];
let kt: Vec<f32> = vec![0.1; batch * heads * dim * seq];
let attn = Matrix::batched_matmul_4d(&q, &kt, batch, heads, seq, dim, seq).unwrap();

// Eigendecomposition (PCA, spectral analysis)
let cov = Matrix::from_vec(2, 2, vec![3.0, 1.0, 1.0, 3.0]).unwrap();
let eigen = SymmetricEigen::new(&cov).unwrap();
let eigenvalues = eigen.eigenvalues();  // [4.0, 2.0]

Performance

Benchmark Results (AMD Ryzen 9 7950X)

GPU Note: GPU acceleration benefits matrix multiply only. Element-wise operations use CPU SIMD (GPU transfer overhead exceeds compute time).

trueno-gpu: Pure Rust CUDA

Generate CUDA PTX kernels without nvcc, LLVM, or external toolchains:

use trueno_gpu::kernels::{GemmKernel, Kernel, SoftmaxKernel};

// Generate optimized GEMM kernel (supports sm_121 Blackwell via PTX 8.8)
let gemm = GemmKernel::tensor_core(1024, 1024, 1024);
let ptx = gemm.emit_ptx();  // Pure Rust PTX generation

// Generate softmax with warp shuffle reduction
let softmax = SoftmaxKernel::new(4096);
let ptx = softmax.emit_ptx();

// Available kernels: GEMM, Softmax, LayerNorm, Attention, Quantize (Q4K/Q5K/Q6K)
// Reliable PTX->cubin compilation via fixed cuLinkCreate path

Training (WGPU)

trueno now supports backward pass computation via WGSL compute shaders, enabling neural network training on AMD, Intel Arc, and Apple Silicon GPUs through Vulkan, Metal, DX12, and WebGPU -- no CUDA required.

7 backward ops implemented:

• silu_backward -- SiLU activation gradient
• gemm_backward_a -- weight gradient (dL/dA)
• gemm_backward_b -- input gradient (dL/dB)
• rmsnorm_backward -- RMSNorm gradient
• rope_backward -- rotary position embedding gradient
• adamw_step -- AdamW optimizer parameter update
• nf4_dequant -- NF4 4-bit dequantization for QLoRA

All 7 shaders verified on AMD Radeon Pro W5700X via Vulkan with 8 FALSIFY contract tests passing.

use trueno::backends::gpu::GpuDevice;

let dev = GpuDevice::new()?;

// Backward pass: compute SiLU gradient
dev.silu_backward(&input, &grad_output, &mut grad_input)?;

// Optimizer step: AdamW update
dev.adamw_step(&mut params, &grads, &mut m, &mut v, lr, beta1, beta2, eps, weight_decay, step)?;

Operations

Vector: add, sub, mul, div, dot, sum, min, max, argmin, argmax, norm_l1, norm_l2, normalize, recip, sqrt, abs, clamp

Activations: relu, leaky_relu, elu, sigmoid, tanh, gelu, swish, softmax, log_softmax, silu

Matrix: matmul, batched_matmul, batched_matmul_4d, transpose, matvec, convolve2d, pooling (max/avg), topk, gather, pad

Statistics: mean, variance, stddev, covariance, correlation, zscore

Eigen: symmetric eigendecomposition (Jacobi algorithm)

GPU Kernels: GEMM (naive/tiled/tensor core), Softmax, LayerNorm, RMSNorm, Attention, GEMV, Quantization

Development

cargo test                  # Run tests (3,434+ passing, 3,438 with CUDA)
cargo bench                 # Run benchmarks
make coverage              # Coverage report (requires cargo-llvm-cov)
cargo run --example backend_detection  # Check available backends

Ecosystem

Part of the Pragmatic AI Labs stack:

• trueno-gpu - Pure Rust PTX generation (no nvcc)
• trueno-db - GPU-first analytics database
• trueno-graph - Graph algorithms
• trueno-rag - RAG pipeline
• 🤖 Coursera Hugging Face AI Development Specialization - Build Production AI systems with Hugging Face in Pure Rust

Usage

Add trueno to your Cargo.toml:

[dependencies]
trueno = "0.16"

Then use it in your code:

use trueno::Vector;

let a = Vector::from_slice(&[1.0, 2.0, 3.0]);
let b = Vector::from_slice(&[4.0, 5.0, 6.0]);
let result = a.add(&b).unwrap();

The library auto-selects the best SIMD backend at runtime. No configuration needed.

Contributing

Contributions are welcome. Please ensure:

1. All tests pass: cargo test --all-features
2. Coverage stays above 90%: make coverage
3. No clippy warnings: cargo clippy --all-features -- -D warnings
4. Code is formatted: cargo fmt

MSRV

Minimum Supported Rust Version: 1.89

See Also

• Cookbook — 34 runnable examples

License

MIT - see LICENSE