blas-with-sparse

Implementing matrix calculation using different BLAS and test time performance.

use command line

sudo apt-get install libopenblas-dev

if everything goes fine, openblas will be installed into /usr/lib/

Please follow Intel's instruction If nothing wrong happens, mkl will be installed into /opt/intel/mkl/

git clone https://github.com/hyichao/blas-with-sparse.git

Use Openblas to test common matrix calculation as baseline

Inside the project，two shell script is available.

If you want to run with openblas, please enter the project and run

sh run-with-openblas.sh

The path and link flag may not be suitable for someone who install openblas in custom way. If so, modify the path.

Different BLAS offer same API for most often used calculation. i.e. Matrix * Vector

cblas_sgemv()
cblas_dgemv()
...

Just to mention, when letter 'd' is put in front of 'gemv', data is stored as double; when the letter is 's', data is stored as float.

Inside the project，two shell script is available.

If you want to run with openblas, please enter the project and run

sh run-with-mkl.sh

The path and link flag may not be suitable for someone who install mkl in custom way. If so, modify the path.

When it comes to neural network compression issue, the most important topic is how to do calculation with sparse matrix.

mkl offers API

mkl_cspblas_?csrgemv ()

This API require matrix data stored in CSR format.
About CSR format, I personally recommand reading this pdf.

Same with above

Testing time with 1000 iterations of 1024*11985 matrix