This page of IMSL says
To obtain improved performance we recommend linking with High Performance versions of LAPACK and BLAS, if available.
What is High Performance versions of LAPACK and BLAS ?
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user15964
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1for example Atlas library, OpenBlas, Intel MKL – ztik Nov 24 '15 at 14:08
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@ctheo Thank you so much – user15964 Nov 24 '15 at 14:47
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1@ctheo and how do they compared to the standard Lapack and Blas? Did they modify the source code? Will they less robust than the standard Lapack and Blas? – user15964 Nov 24 '15 at 14:53
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1they are optimized for the CPU architecture. The interface is exactly the same with standard `BLAS`. I would suggest to test them. – ztik Nov 24 '15 at 14:57
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There are plenty of good implementations to pick from:
- Intel MKL is likely the best on Intel machines. It's not free though, so that may be a problem.
- According to their benchmark, OpenBLAS compares quite well with Intel MKL and is free
- Eigen is also an option and has a largish (albeit old) benchmark showing good performance on small matrices (though it's not technically a drop-in BLAS library)
- ATLAS, OSKI, POSKI are examples of auto-tuned kernels which will claim to work on many architectures
Generally, it is quite hard to pick one of these without benchmarking because:
- some implementations work better on different types of matrices. For example Eigen works better on matrices with small rank (100s)
- some are optimised for specific architectures (e.g. Intel's)
- in some cases the multithreading of the BLAS library may conflict with a multithreaded application (e.g. OpenBLAS)
- developer's benchmarks may tend to emphasise cases which work better on their implementation.
I would suggest pick one or two of these libraries that apply for your use case and benchmark them for your particular application on your particular (or similar) machine. This is quite easy to do even after compiling your code.
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LAPACK and BLAS are performance libraries that provides basically Linear Algebra mathematical operations for a system of linear equations. you can find such libraries useful in the computer vision for example ( Object detection and classifications ) , Classical algorithms, Modelling , ...
TAsking provides a full C implementation of the LAPACK and BLAS performance libraries, both libraries are ISO-C99 Compliant with full documentation and examples, you can check it here http://www.tasking.com/products/tasking-lapack-performance-libraries
Ahmad Barakat
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