matmul-bench#
Compares matrix-matrix multiply (GEMM) performance across implementations running in the browser, plus a native reference measured outside it.
Live: https://concept-collection.github.io/matmul-bench/
Methods:
- JavaScript/TypeScript — plain triple loop over
Float64Array(f64), no special-casing. The "no special effort" baseline. - WebGPU — a tiled compute shader (f32; WGSL has no double precision).
- WASM from custom C — naive, blocked+SIMD (
f64x2,-msimd128), and a threaded (WASM pthreads) version of the blocked+SIMD kernel, built with emscripten (f64). The threaded one needs cross-origin isolation. - libFLAME/BLIS in WASM — a real BLAS (
dgemmvia libFLAME + BLIS) compiled to WebAssembly, single-threaded and multi-threaded (WASM pthreads), from libflame2wasm (f64). The threaded build needs cross-origin isolation (SharedArrayBuffer), supplied on GitHub Pages by a vendored service worker. - Native LAPACK/OpenBLAS — a fixed reference table from
dgemmrun outside the browser (native/).
Develop#
npm install
npm run dev # local dev server (sends COOP/COEP for threaded methods)
wasm/build-wasm.sh # rebuild wasm/dist/matmul.* + public/matmul/matmul_mt.*
# (needs emsdk)
# One-time: build the WASM libflame/BLIS libraries in a sibling checkout
git clone https://github.com/concept-collection/libflame2wasm ../libflame2wasm
(cd ../libflame2wasm && ./build-all-wasm.sh) # slow; builds libflame twice
blis/build-blis-wasm.sh # rebuild public/matmul/matmul_blis_{st,mt}.*
# (needs emsdk + the sibling libflame2wasm above;
# override its path with LIBFLAME=/path/to/libflame2wasm)
npm run build # tsc -b && vite build
Nothing prebuilt is committed. The WASM modules under public/matmul/ (except
worker.js) are built by CI: the custom kernels from wasm/matmul.c, and the
libFLAME/BLIS modules by cloning
libflame2wasm as a
sibling, running its build-all-wasm.sh (cached, keyed on its commit SHA), and
linking against the resulting .a libraries.
Native reference#
native/build.sh
OPENBLAS_NUM_THREADS=1 native/bench_native 128 256 512 1024 2048
OPENBLAS_NUM_THREADS=12 native/bench_native 128 256 512 1024 2048
Hand-copy the results into src/data/nativeReference.ts.