sklears-simd

Latest release: 0.1.0-beta.1 (January 1, 2026). See the workspace release notes for highlights and upgrade guidance.

Overview

sklears-simd exposes low-level SIMD, GPU, and hardware acceleration utilities used across the sklears ecosystem. While primarily an internal crate, it is documented for contributors building new high-performance components.

Key Features

• Vector Abstractions: Portable SIMD types (f32x4, f32x8, f32x16) with architecture-specific intrinsics.
• Alignment & Memory: Alignment helpers, prefetching hints, cache-aware allocation strategies.
• GPU Bridges: CUDA/WebGPU adapters, Tensor Core pathways, and multi-GPU orchestration helpers.
• Benchmark Harnesses: Criterion-based benchmarks and profiling utilities for micro-optimizations.

Quick Peek

use sklears_simd::vector::F32x4;

let a = F32x4::new(1.0, 2.0, 3.0, 4.0);
let b = F32x4::splat(2.0);
let result = a.mul(b);
assert_eq!(result.horizontal_sum(), 20.0);

Status

• Core building block validated by the 11,292 passing workspace tests for 0.1.0-beta.1.
• Powers SIMD/GPU acceleration in linear models, neighbors, metrics, and more.
• Contributor roadmap (new architectures, auto-vectorization tooling) maintained in TODO.md.