https://github.com/flame/blislab

BLISlab: A Sandbox for Optimizing GEMM
https://github.com/flame/blislab

blis code-optimization gemm matrix-multiplication

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BLISlab: A Sandbox for Optimizing GEMM

• Host: GitHub
• URL: https://github.com/flame/blislab
• Owner: flame
• Created: 2016-04-20T18:59:24.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2021-06-17T16:50:33.000Z (over 3 years ago)
• Last Synced: 2024-08-02T14:05:05.812Z (3 months ago)
• Topics: blis, code-optimization, gemm, matrix-multiplication
• Language: C
• Homepage:
• Size: 6.8 MB
• Stars: 461
• Watchers: 16
• Forks: 98
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-gemm - BLISlab - like GEMM algorithms. (Frameworks)
• awesome-cuda-and-hpc - flame/blislab
• awesome-cuda-and-hpc - flame/blislab

README

        
# BLISlab: A Sandbox for Optimizing GEMM

Matrix-matrix multiplication is a fundamental operation of great

importance to scientific computing and, increasingly, machine learning.

It is a simple enough concept to be introduced in a typical high school

algebra course yet in practice important enough that its implementation

on computers continues to be an active research topic. This note

describes a set of exercises that use this operation to illustrate how

high performance can be attained on modern CPUs with hierarchical

memories (multiple caches). It does so by building on the insights that

underly the [BLAS-like Library Instantiation Softare (BLIS) framework](https://github.com/flame/blis) by

exposing a simplified “sandbox” that mimics the implementation in BLIS.

As such, it also becomes a vehicle for the “crowd sourcing” of the

optimization of BLIS. We call this set of exercises [BLISlab](https://github.com/flame/blislab).

Check the [tutorial](https://github.com/flame/blislab/blob/master/tutorial.pdf) for more details.

# Related Links

* [How to Optimize GEMM Wiki] (https://github.com/flame/how-to-optimize-gemm/wiki)

* [GEMM: From Pure C to SSE Optimized Micro Kernels] (http://apfel.mathematik.uni-ulm.de/~lehn/sghpc/gemm/)

# Citation

For those of you looking for the appropriate article to cite regarding BLISlab, we

recommend citing our

[TR](http://arxiv.org/pdf/1609.00076v1.pdf): 

```

@TechReport{FLAWN80,

  author = {Jianyu Huang and Robert A. van~de~Geijn},

  title = {{BLISlab}: A Sandbox for Optimizing {GEMM}},

  institution = {The University of Texas at Austin, Department of Computer Science},

  type = {FLAME Working Note \#80,},

  number = {TR-16-13},

  year = {2016},

  url = {http://arxiv.org/pdf/1609.00076v1.pdf}

}

``` 

# Acknowledgement

This material was partially sponsored by grants from the National Science Foundation (Awards ACI-1148125/1340293 and ACI-1550493).

_Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF)._