LAK

LAK is a small personal linear algebra crate with BLAS-like kernels over contiguous Rust slices. Its goal was to see whether a safe, contiguous-only Rust library can stay minimal and elegant without sacrificing much performance. Benchmarks are available here. The current blocking constants and chunk sizes are tuned for ARMv8/Apple Silicon. If you test LAK on another architecture and find better parameters, tuning contributions are welcome.

The API is organized around BLAS levels:

lak::l1: vector-vector routines like dot, axpy, scal, and nrm2
lak::l2: matrix-vector routines like gemv, ger, trmv, and trsv
lak::l3: matrix-matrix routines like gemm

Matrices are column-major. The safe API uses MatRef, MatMut, VecRef, and VecMut view types from lak::types. All Level-1 and Level-2 routines are generic over f32/f64. Level-3 gemm is also generic, but direct sgemm and dgemm functions exist for maximum performance.

LAK also provides lak::blas, a thin wrapper around the historical BLAS API for both LP64 and ILP64 callers. Though since LAK is contiguous-only, strides are asserted to be 1.

The only Level-3 routine implemented right now is gemm.

use lak::l3::gemm;
use lak::types::{MatMut, MatRef, Transpose};

// column-major 2 x 3 matrix:
// [1 3 5]
// [2 4 6]
let a = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];

// column-major 3 x 2 matrix:
// [ 7 10]
// [ 8 11]
// [ 9 12]
let b = [7.0, 8.0, 9.0, 10.0, 11.0, 12.0];

let mut c = [0.0, 0.0, 0.0, 0.0];

let alpha = 1.0;
let beta = 0.0;

let a = MatRef::new(&a, (2, 3));
let b = MatRef::new(&b, (3, 2));
let mut c = MatMut::new(&mut c, (2, 2));

// c.reborrow() used to allow c.as_slice() afterwards.
gemm(
    Transpose::NoTranspose,
    Transpose::NoTranspose,
    alpha,
    beta,
    a,
    b,
    c.reborrow(),
);

// C = A * B
// [ 76 103]
// [100 136]
assert_eq!(c.as_slice(), &[76.0, 100.0, 103.0, 136.0]);

This crate currently targets nightly Rust because it uses portable_simd.

Informal notes on LAK’s design, implementation challenges, and lessons learned are available here.

lak-kernels 0.1.1

LAK