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mratsim
weave
Nim2019–2025
mratsim / weave

weave

A state-of-the-art multithreading runtime: message-passing based, fast, scalable, ultra-low overhead

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9Distinct people
30Mentions
0Explicit recommenders
587GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A state-of-the-art multithreading runtime: message-passing based, fast, scalable, ultra-low overhead

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:49:33 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year30 dated mentions

Observed mentions: 2019-11-07 21:10:13 UTC to 2025-03-04 17:23:57 UTC.

051015202019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 18 mentions; 1 submissions, 17 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC18117
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC404
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC505
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101

What Hacker News says

5 sources
mratsim·Jan 4, 2024
First we can use Laser, which was my initial BLAS experiment in 2019. At the time in particular, OpenBLAS didn't properly use the AVX512 VPUs. (See thread in BLIS https://github.com/flame/blis/issues/352 ), It has made progress since then, still, on my current laptop perf is in the same range Reproduction: - Assuming x86 and preferably Linux. - Install Nim - Install a C compiler with OpenMP support (not the default MacOS Clang) - Install git The repo submodules MKLDNN (now Intel oneDNN) to bench vs Intel JIT Compiler ``` git clone https://github.com/mratsim/laser cd laser git submodule update --init --recursive nim cpp -r --outdir:build -d:danger -d:openmp benchmarks/gemm/gemm_bench_float32.nim ``` This should output something like this ``` Laser production implementation Collected 10 samples in 0.230 seconds Average time: 22.684 ms Stdde
Read the rest
v time: 0.596 ms Min time: 21.769 ms Max time: 23.603 ms Perf: 624.037 GFLOP/s OpenBLAS benchmark Collected 10 samples in 0.216 seconds Average time: 21.340 ms Stddev time: 3.334 ms Min time: 19.346 ms Max time: 27.502 ms Perf: 663.359 GFLOP/s MKL-DNN JIT AVX512 benchmark Collected 10 samples in 0.201 seconds Average time: 19.775 ms Stddev time: 8.262 ms Min time: 15.625 ms Max time: 43.237 ms Perf: 715.855 GFLOP/s ``` Note: the Theoretical peak limit is hardcoded and used my previous machine i9-9980XE. It maybe that your BLAS library is not named libopenblas.so, you can change that here: https://github.com/mratsim/laser/blob/master/benchmarks/thir... Implementation is in this folder: https://github.com/mratsim/laser/tree/master/laser/primitive... in particular, tiling, cache and register optimization: https://github.com/mratsim/laser/blob/master/laser/primitive... AVX512 code generator: https://github.com/mratsim/laser/blob/master/laser/primitive... And generic Scalar/SSE/AVX/AVX2/AVX512 microkernel generator (this is Nim macros to generate code at compile-time): https://github.com/mratsim/laser/blob/master/laser/primitive... I'll come back later with details on how to use my custom HPC threadpool Weave instead of OpenMP (https://github.com/mratsim/weave/tree/master/benchmarks/matm...). As a side bonus it also has parallel nqueens implemented.
In Benchmarking 20 programming languages on N-queens and matrix multiplication
mratsim·Jan 3, 2024
> People use matrix multiplication libraries (often written in Assembly) from every language if they really care about performance. That's because such libraries incorporate 100 PhD theses' worth of tricks that no individual can hope to reinvent in the course of solving another problem. There is absolutely nothing special about Python in this context. You don't have to use Assembly. Case in point, this is as fast as OpenBLAS: https://github.com/mratsim/weave/tree/master/benchmarks/matm...
In Benchmarking 20 programming languages on N-queens and matrix multiplication

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