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A comment on Outperforming LAPACK with C++ metaprogramming. Parent comment

Re:Outperforming LAPACK with C++ metaprogramming by electricslpnsld on Monday January 29, 2018 @01:10PM (#16259347)
A better comparison might be a C++ library like Eigen that is intended for use on these small systems, that should optimize out function calls in a similar way, and that uses intrinsics.
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Re:Outperforming LAPACK with C++ metaprogramming by stochastic_monk on Monday January 29, 2018 @02:17PM (#16259910)
If your matrices are of arbitrary size, Blaze [0] would be my pick. It has the best performance on their benchmarks, Baptiste Wicht's benchmarks, and my own.

Though if the matrices are guaranteed to be small (like in this example), libxsmm [1] is specialized and highly optimized for this use case and beats its competitors above.

And yes, it's absolutely essential to make sure you didn't just move computations to compile-time.

[0]: https://bitbucket.org/blaze-lib/blaze

[1]: https://github.com/hfp/libxsmm

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