Question

4

Why are there so many OpenBLAS packages and which one would yield fastest results?

rated 0 times [ 4] [ 0] / answers: 1 / hits: 4566 / 2 Years ago, sun, august 28, 2022, 2:16:05

In Ubuntu 20.04, there are many packages for OpenBLAS.

~$ apt search openblas

p   libopenblas-base                              - Optimized BLAS (linear algebra) library (transitional)                                               

p   libopenblas-dev                               - Optimized BLAS (linear algebra) library (dev, meta)                                                  

p   libopenblas-openmp-dev                        - Optimized BLAS (linear algebra) library (dev, openmp)                                                

p   libopenblas-pthread-dev                       - Optimized BLAS (linear algebra) library (dev, pthread)                                               

p   libopenblas-serial-dev                        - Optimized BLAS (linear algebra) library (dev, serial)                                                

i A libopenblas0                                  - Optimized BLAS (linear algebra) library (meta)                                                       

p   libopenblas0-openmp                           - Optimized BLAS (linear algebra) library (shared lib, openmp)                                         

i A libopenblas0-pthread                          - Optimized BLAS (linear algebra) library (shared lib, pthread)                                        

p   libopenblas0-serial                           - Optimized BLAS (linear algebra) library (shared lib, serial)                                         

p   libopenblas64-0                               - Optimized BLAS (linear algebra) library (shared lib, 64bit, meta)                                    

p   libopenblas64-0-openmp                        - Optimized BLAS (linear algebra) library (shared lib, 64bit, openmp)                                  

p   libopenblas64-0-pthread                       - Optimized BLAS (linear algebra) library (shared lib, 64bit, pthread)                                 

p   libopenblas64-0-serial                        - Optimized BLAS (linear algebra) library (shared lib, 64bit, serial)                                  

p   libopenblas64-dev                             - Optimized BLAS (linear algebra) library (dev, 64bit, meta)                                           

p   libopenblas64-openmp-dev                      - Optimized BLAS (linear algebra) library (dev, 64bit, openmp)                                         

p   libopenblas64-pthread-dev                     - Optimized BLAS (linear algebra) library (dev, 64bit, pthread)                                        

p   libopenblas64-serial-dev                      - Optimized BLAS (linear algebra) library (dev, 64bit, serial)

Which of these packages would yield fastest results?

I intend to do numerical computation (mostly diagonalizing matrices) in GNU Octave. My computer has a Intel Core i3-5005U processor, (in case the optimized package should depend on processor type, please mention which package should be preferred on other types of processors).

I have noticed that there is at least 10x speed improvement when OpenBLAS is used instead of default BLAS.

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answered 2 Years ago fulpu · Accepted Answer

The answer to "why?" question may be - to get universal solution for many CPUs and platforms.

Technically all these binary packages came from the same openblas source package.

If we talk about provided library variants for update-alternatives, then after sudo apt-get install "*openblas*" we can count 4 groups with 4 choices:

$ sudo update-alternatives --config libopenblas<Tab>

libopenblas64.so.0-x86_64-linux-gnu  libopenblas64.so-x86_64-linux-gnu  

libopenblas.so.0-x86_64-linux-gnu    libopenblas.so-x86_64-linux-gnu

After installation the pthread is set to default (0 selection) version.

For basic benchmarking we can utilize our old mkl-test.sh script for different alternatives for openblas libraries using update-alternatives.

Below are results for my i7-3537U, for third run (lower is faster, all results are in seconds):

Alt library	Scilab	Julia	Python 3 with NumPy	R	Octave
`pthread`	0.31	0.76	0.31	0.39	0.31
`openmp`	0.24	0.75	0.22	0.31	0.22
`serial`	0.17	0.79	0.17	0.27	0.17
atlas/liblapack	0.31	0.75	0.32	0.52	0.32
lapack/liblapack	0.26	0.76	0.30	0.47	0.28
libmkl_rt (MKL)	0.16	0.76	0.16	0.22	0.16

Better way is to run official benchmarks, but I can't currently understand how to run them.