HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
flame
blis
C2014–2024
flame / blis

blis

BLAS-like Library Instantiation Software Framework

Science & EngineeringLibraries & SDKs
Explore on GitHubRead Hacker News discussions
19Distinct people
33Mentions
0Explicit recommenders
2.7KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

BLAS-like Library Instantiation Software Framework

GitHub

Where it is used

No documented product use was verified in the last source check.

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 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 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 year33 dated mentions

Observed mentions: 2014-12-20 03:22:12 UTC to 2024-08-01 09:37:03 UTC.

024682014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20142015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 8 mentions; 1 submissions, 7 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 5 mentions; 2 submissions, 3 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 6 mentions; 1 submissions, 5 comments2024

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
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC101
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC000
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC211
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC202
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC817
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC505
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC211
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC523
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC202
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC615

What Hacker News says

33 sources
sevagh·Apr 24, 2024
BLIS is an interesting new direction in that regard: https://github.com/flame/blis >The BLAS-like Library Instantiation Software (BLIS) framework is a new infrastructure for rapidly instantiating Basic Linear Algebra Subprograms (BLAS) functionality. Its fundamental innovation is that virtually all computation within level-2 (matrix-vector) and level-3 (matrix-matrix) BLAS operations can be expressed and optimized in terms of very simple kernels.
In Faer-rs: Linear algebra foundation for Rust
quanto·Jan 24, 2024
The real money shot is here: https://github.com/flame/blis/blob/master/docs/Performance.m... It seems that the selling point is that BLIS does multi-core quite well. I am especially impressed that it does as well as the highly optimized Intel MKL on Intel CPUs. I do not see the selling point of BLIS-specific APIs, though. The whole point of having an open BLAS API standard is that numerical libraries should be drop-in replaceable, so when a new library (such as BLIS here) comes along, one could just re-link the library and reap the performance gain immediately. What is interesting is that numerical algebra work, by nature, is mostly embarrassingly parallel, so it should not be too difficult to write multi-core implementations. And yet, BLIS here performs so much better than some other industry-leading implementations on multi-core conf
Read the rest
igurations. So the question is not why BLIS does so well; the question is why some other implementations do so poorly.
In BLIS: A BLAS-like framework for basic linear algebra routines
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
bee_rider·May 18, 2023
I’m sure it depends on the subroutine. Reference BLAS is of course Fortran but nobody would actually use it. Some other good BLAS are (in order of historical to cutting edge), ATLAS, OpenBLAS, and then BLIS. They all mostly use C and assembly. BLIS makes a good BLAS. It almost keeps up with MKL, according to the nice plots on their GitHub. https://github.com/flame/blis/blob/master/docs/Performance.m... They use C mostly, because the magic is in the GEMM (and possibly TRSM) kernels, which you are going to write in assembly anyway. Intel also released oneDNN open source, which has an awful lot of BLAS-like functionality. No Fortran in there. MKL is older than all these libraries of course (maybe not ATLAS, I’m not sure actually), and has a lot of other functionality (sparse stuff, FFTs), so I’m sure it has more Fortran! But it would be aty
Read the rest
pical compared to the rest of the BLAS implementations, if, like, their DGEMM was Fortran.
In SciPy: Interested in adopting PRIMA, but little appetite for more Fortran code
sfpotter·Oct 27, 2022
Here's BLIS's object API: https://github.com/flame/blis/blob/master/docs/BLISObjectAPI... Searching "object" in BLIS's README (https://github.com/flame/blis) to see what they think of it: "Objects are relatively lightweight structs and passed by address, which helps tame function calling overhead." "This is API abstracts away properties of vectors and matrices within obj_t structs that can be queried with accessor functions. Many developers and experts prefer this API over the typed API." In my opinion, this API is a strict improvement over BLAS. I do not think there is any reason to prefer the old BLAS-style API over an improvement like this. Regarding your own experience, it's great that using BLAS proved to be a valuable learning experience for you. But your argument that the BLAS API is somehow uniquely helpful in terms of learning ho
Read the rest
w to program numerical algorithms efficiently, or that it will help you avoid performance problems, is not true. It is possible to replace the BLAS API with a more modern and intuitive API with the same benefits. To be clear, the benefits here are direct memory management and control of striding and matrix layout, which create opportunities for optimization. There is nothing unique about BLAS in this regard---it's possible to learn these lessons using any of the other listed options if you're paying attention and being systematic.
In Column Vectors vs. Row Vectors
gnufx·Apr 14, 2022
The article asks "Which Micro-optimizations matter for BLAS3?", implying small dimensions, but doesn't actually tell me. The problem is well-studied, depending on what you consider "small". The most important thing is to avoid the packing step below an appropriate threshold. Implementations include libxsmm, blasfeo, and the "sup" version in blis (with papers on libxsmm and blasfeo). Eigen might also be relevant. https://libxsmm.readthedocs.io/ https://blasfeo.syscop.de/ https://github.com/flame/blis
In Doing small network scientific machine learning in Julia faster than PyTorch
gnufx·Dec 18, 2021
However, on recent CPUs 4x4 is small for the innermost block size of the non-trivial hierarchy you need. You can see examples under https://github.com/flame/blis/tree/master/config with an a priori procedure for determining them in https://www.cs.utexas.edu/users/flame/pubs/TOMS-BLIS-Analyti... (but compare with what's actually used for SKX, in particular). OpenBLAS will normally be similar, though it may come out somewhat faster, but it's easier to see in BLIS.
In Matrix Multiplication Inches Closer To Mythic Goal

Showing the 15 most recent of 33 sources. Enable JavaScript to search and browse every indexed mention.

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.