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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 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 11:34:28 AM 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

8 sources
lhl·Dec 8, 2019
Everything on that benchmark page is listed as GFLOPS/core so at 30GFLOPS that Epyc 7551 should be pushing around 2 TFLOPS. You can take a look at reproducing them if you want if you have a similar system: https://github.com/flame/blis/blob/master/docs/Performance.m... I ran into some compile issues, but I care more about numpy so I gave this script a try: https://markus-beuckelmann.de/blog/boosting-numpy-blas.html with some quick/dirty results on my Ryzen 3700X (8C16T) workstation (BLIS doesn't seem to perform very well on this small test). You can see performance basically double on MKL when MKL_DEBUG_CPU_TYPE=5 is used. I'll probably continue to stick w/ OpenBLAS for now: ## blis 0.6.0 h516909a_0 # conda activate numpy-blis # conda run python bench.py Dotted two 4096x4096 matrices in 2.30 s. Dotted two vectors of length 52428
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8 in 0.08 ms. SVD of a 2048x1024 matrix in 0.94 s. Cholesky decomposition of a 2048x2048 matrix in 0.24 s. Eigendecomposition of a 2048x2048 matrix in 6.36 s. ## libopenblas 0.3.7 h5ec1e0e_4 # conda activate numpy-openblas # conda run python bench.py Dotted two 4096x4096 matrices in 0.41 s. Dotted two vectors of length 524288 in 0.02 ms. SVD of a 2048x1024 matrix in 0.55 s. Cholesky decomposition of a 2048x2048 matrix in 0.14 s. Eigendecomposition of a 2048x2048 matrix in 5.53 s. ## mkl 2019.4 243 # conda activate numpy-mkl # conda run python bench.py Dotted two 4096x4096 matrices in 1.53 s. Dotted two vectors of length 524288 in 0.02 ms. SVD of a 2048x1024 matrix in 0.51 s. Cholesky decomposition of a 2048x2048 matrix in 0.29 s. Eigendecomposition of a 2048x2048 matrix in 4.79 s. # export MKL_DEBUG_CPU_TYPE=5 # conda run python bench.py Dotted two 4096x4096 matrices in 0.33 s. Dotted two vectors of length 524288 in 0.02 ms. SVD of a 2048x1024 matrix in 0.29 s. Cholesky decomposition of a 2048x2048 matrix in 0.15 s. Eigendecomposition of a 2048x2048 matrix in 3.29 s.
In Reducing the Performance Gap of Intel's MKL on AMD Threadripper
gnufx·Dec 2, 2019
The advantage of MKL is typically greatly over-rated anyhow, and I don't see why one should care. BLIS and OpenBLAS have good tuned x86 BLAS implementations, and run infinitely faster than MKL on ARM and POWER, for instance, and if you're interested in small matrices on x86_64, there's libxsmm. (I know MKL has more than BLAS, but I don't know what there is that doesn't have free rough equivalents.) BLIS performance: https://github.com/flame/blis/blob/master/docs/Performance.m...
In Puget Systems:“Intel Has Every Right” to Make Intel MKL Run Slower on AMD CPUs
gnufx·May 14, 2019
Not arguing with that, but I think the jury is out on whether they need to be hand vectorized. With recent GCC, generic C for BLIS' DGEMM gives about 2/3 the performance of the hand-coded version on Haswell, and it may be somewhat pessimized by hand-unrolling rather than letting the compiler do it. The remaining difference is thought to be mainly from prefetching, but I haven't verified that. (Details are scattered in the BLIS issue tracker earlier this year.) For information of anyone who doesn't know about performance of level 3 BLAS: It doesn't come just from vectorization, but also cache use with levels of blocking (and prefetch). See the material under https://github.com/flame/blis. Other levels -- not matrix-matrix -- are less amenable to fancy optimization, with lower arithmetic density to pit against memory bandwidth, and BLI
Read the rest
S mostly hasn't bothered with them, though OpenBLAS has.
In Numpy Clone in Common Lisp

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