sp-matmul-rs 0.2.1

Sparse CSR x CSR matrix multiplication with fused top-n selection (Rust).

sp_matmul_rs

sp_matmul_rs provides a fast way to perform a sparse matrix multiplication (SpGEMM) followed by top-n multiplication result selection.

It is a Rust port of sparse_dot_topn, created and optimised for use in string_grouper — where the dominant workload is matching every row of a very large TF-IDF matrix against every other row and keeping only the top-n nearest neighbours.

sparse_dot_topn is based on Gustavson's algorithm but retains only the best n values per output row. sp_matmul_rs retains the same algorithmic core but is built around L1/L2 cache-blocking: the default driver column-chunks B so the dense per-row accumulator and the streamed B fragments stay resident in L1 (chunk sizes are automatically calculated based on cache size), which is the dominant performance lever once the working set stops fitting in cache. Parallelism is provided by rayon (enabled by default) and the Python distribution releases the GIL around the kernel. On Apple M5 Pro over a 663k × 193k TF-IDF self-similarity workload sp_matmul_rs runs in as little as 41% of the time of sparse_dot_topn when retaining the top 10 values per row and utilising 10 cores. See the benchmark directory for details.

Usage

sp_matmul_topn supports {CSR, CSC, COO} matrices with {32, 64}bit {int, float} data. Note that COO and CSC inputs are converted to the CSR format and are therefore slower. The Python surface (sp_matmul, sp_matmul_topn, zip_sp_matmul_topn) mirrors sparse_dot_topn, so migration from the C++ extension is typically a one-line import swap. Note that sp_matmul_topn(A, B, top_n=B.shape[1]) is equal to sp_matmul(A, B) and A.dot(B).

import numpy as np
import scipy.sparse as sparse
from sp_matmul_rs import sp_matmul, sp_matmul_topn

A = sparse.random(1000, 100, density=0.1, format="csr", dtype=np.float64)
B = sparse.random(100, 2000, density=0.1, format="csr", dtype=np.float64)

# Compute C and retain the top 10 values per row
C = sp_matmul_topn(A, B, top_n=10)

# or parallelised matrix multiplication without top-n selection
C = sp_matmul(A, B, n_threads=2)
# or with top-n selection
C = sp_matmul_topn(A, B, top_n=10, n_threads=2)
# pass n_threads=-1 to use all but one physical core
C = sp_matmul_topn(A, B, top_n=10, n_threads=-1)

# If you are only interested in values above a certain threshold
C = sp_matmul_topn(A, B, top_n=10, threshold=0.8)

# If you set the threshold we cannot easily determine the number of non-zero
# entries beforehand. Therefore, we allocate memory for `ceil(top_n * A.shape[0] * density)`
# non-zero entries. You can set the expected density to reduce the amount pre-allocated
# entries. Note that if we allocate too little an expensive copy(ies) will need to happen.
C = sp_matmul_topn(A, B, top_n=10, threshold=0.8, density=0.1)

The package imports as sp_matmul_rs and does not share its namespace with sparse_dot_topn — both can be installed side-by-side in the same environment.

Installation

sp_matmul_rs provides wheels for CPython 3.9 to 3.13 for:

• Windows (64bit)
• Linux (x86_64 and aarch64, both manylinux and musllinux)
• macOS (x86_64 and ARM)

pip install sp_matmul_rs

sp_matmul_rs relies on a Rust extension for the computationally intensive multiplication routine. Note that the wheels ship with rayon enabled, providing parallelisation out-of-the-box. If you need to disable threading at runtime, omit the n_threads argument (or pass n_threads=1).

Installing from source requires a Rust toolchain (1.75+) and maturin:

pip install maturin
pip install sp_matmul_rs --no-binary sp_matmul_rs

Supported

Values	Indices	Python	Platforms
f32, f64, i32, i64	i32, i64	3.9 – 3.13	manylinux x86_64/aarch64, musllinux x86_64, macOS x86_64/arm64, Windows x86_64

Rust crate

For Rust callers, the same kernels are available without the Python bindings:

[dependencies]
sp_matmul_rs = "0.1"

use sp_matmul_rs::{sp_matmul_topn, CsrView, SortMode, TopNOptions};

let c = sp_matmul_topn::<f64, i32>(
    a_view,
    b_view,
    10,
    TopNOptions { sort: SortMode::ByValue, ..Default::default() },
);

Features

Feature	Default	What it enables
rayon	yes	Parallel column-chunked driver (parallel::*).
python	no	PyO3 + numpy bindings — used by the Python distribution.

Module layout

Module	Responsibility
scalar	Scalar trait + impls for f32, f64, i32, i64
index	Index trait + impls for i32, i64
csr	CsrView (borrowed) and CsrMatrix (owned)
maxheap	Bounded max-heap retaining top-n scores per row
matmul	Sequential sp_matmul (no top-n)
matmul_topn	Sequential sp_matmul_topn (single full-column path)
chunked	Column-chunked driver — the L1/L2 cache-blocking pillar
zip	zip_sp_matmul_topn for distributed/cluster results
parallel	Rayon-backed variants (feature rayon)
python	PyO3 + numpy bindings (feature python)

Build from source

# Rust crate
cargo build --release                       # default features: rayon
cargo build --release --no-default-features
cargo test
cargo bench

# Python extension (requires maturin and CPython >=3.9)
pip install maturin
maturin develop --release --features python,rayon
python -c "import sp_matmul_rs; print(sp_matmul_rs.kernel_info())"

x86-64: FMA/AVX2 via runtime dispatch

Baseline x86-64 builds (including the PyPI wheels) cannot assume FMA/AVX2 at compile time, so the chunked kernels ship AVX2+FMA clones selected by runtime CPU detection — worth ~17–18 % on real string-matching workloads, and available on effectively every x86-64 CPU since 2013 (Haswell/Zen). No configuration needed; check which path is active with sp_matmul_rs.kernel_info()['runtime_simd'] ("avx2+fma", "baseline", or "compile-time"), and set SP_MATMUL_RS_FORCE_BASELINE=1 to disable the clones for A/B measurements.

Building from source with RUSTFLAGS="-C target-cpu=native" still applies the full native feature set (including AVX-512 where present — the runtime dispatch tier is AVX2+FMA only) to the whole crate and compiles the dispatch machinery away; on AVX2-class hardware it measures the same as the runtime-dispatched wheel. See bench/results_x86_i7-6500U.md for x86-64 numbers and the chunk_cols / SP_MATMUL_RS_CHUNK_COLS tuning knob.

Benchmarks

bench/bench_rust_vs_cpp_scipy.py is a three-way comparison against the upstream C++ extension (sparse_dot_topn) and scipy.sparse on a word-level TF-IDF matrix over the EDGAR company-name corpus (Kaggle: dattapiy/sec-edgar-companies-list). Numbers below were measured on an Apple M5 Pro (10 performance cores), 48 GB, macOS 26.4, with both libraries built with parallelism enabled (sparse_dot_topn against Homebrew libomp, sp_matmul_rs with rayon). Times are per-call min across timed runs (one untimed warm-up first).

Full EDGAR corpus — A @ A.T (663 000 × 193 190)

The workload that string_grouper actually runs in production: every company name matched against every other. Scipy and the no-top-n baseline are omitted because the dense result is too large to materialise on a 48 GB machine. repeats = 3.

Benchmark	top_n	n_threads	C++ (s)	Rust (s)	Rust vs C++
sp_matmul_topn	10	1	132.537	68.585	1.9x
sp_matmul_topn	10	8	28.143	12.903	2.2x
sp_matmul_topn	10	10	28.429	11.567	2.5x
sp_matmul_topn	20	1	131.878	70.350	1.9x
sp_matmul_topn	20	8	27.505	13.154	2.1x
sp_matmul_topn	20	10	27.524	11.415	2.4x

At workload-realistic scale, sp_matmul_rs consistently runs in 40–50 % of the time of the C++ extension and continues to scale past 8 threads where the OpenMP build plateaus. The L1/L2 cache-blocking driver does the heavy lifting — at 663 000 rows the per-row working set no longer fits in cache, and the chunked layout keeps the dense accumulator L1-resident.

Sampled shape — 20 000 × 193 190 × 20 000

The same shape as the upstream C++ bench README, included for direct comparability. repeats = 5.

Benchmark	top_n	n_threads	Scipy (s)	C++ (s)	Rust (s)	Rust vs Scipy	Rust vs C++
sp_matmul_topn	10	1	0.110	0.102	0.083	1.3x	1.2x
sp_matmul_topn	10	8	0.107	0.014	0.015	6.9x	-1.1x
sp_matmul_topn	30	1	0.108	0.124	0.119	-1.1x	1.0x
sp_matmul_topn	30	8	0.109	0.017	0.021	5.3x	-1.2x
sp_matmul_topn	100	8	0.107	0.031	0.041	2.6x	-1.3x
sp_matmul_topn	1000	8	0.107	0.112	0.180	-1.7x	-1.6x

At this smaller shape the per-row working set still fits in cache, so the C++ extension's flat layout is competitive (Rust trails by ~1.2–1.6× at large top_n). The sampled bench is included for direct comparability with the upstream C++ project's published table — the full-corpus numbers above are the more relevant indicator for real string-matching workloads.

The full {top_n, n_threads} grid (including the plain sp_matmul baseline and Scipy timings) lives in bench/README.md.

License

Apache-2.0. See LICENSE.