[Octave-bug-tracker] [bug #58926] Octave gives wrong results with intel-mkl when diagonalizing large matrices

[Archisman Panigrahi]

Follow-up Comment #32, bug #58926 (project octave):

Re comment #31, I have an entry level Intel i3 (4 cores) CPU.
I am using precompiled OpenBLAS libraries from the Ubuntu repositories. Will
compiling OpenBLAS in my machine significantly improve results? 

There are many threads about it in internet, some advise to compile OpenBLAS
and then compile Octave using that OpenBLAS, while some advise to compile
OpenBLAS and make it the default BLAS, and Octave (precompiled, from Ubuntu
repositories) will automatically use it. 

Can someone suggest which among these two methods will give better results?
Also, what kind of flags should be used while compiling OpenBLAS?

@Dmitri: Is OMP_NUM_THREADS somehow related to OpenBLAS? What is the best
practice for specifying the number of threads? (= number of cores?)

Anyway, here are my results with the precompiled OpenBLAS from default
repositories.


octave:1> format long g
octave:2> clear all; n = 500; C = sin((1:n)' + (1:n).^2); for i = 100:-1:1,
tic; val(i) = max(eig(C)); t(i) = toc; end; clear i; assert(range(val)==0);
assert(all(abs(val - 16.914886497930) <= 1e-12)); [min(t), mean(t), median(t),
max(t), range(t), std(t)]
ans =

 Columns 1 through 3:

     0.417322158813477     0.441691470146179     0.441818952560425

 Columns 4 through 6:

     0.512679100036621    0.0953569412231445    0.0211517261113866



Here are the results with MKL and the environment variable workaround, for the
same code.


ans =

 Columns 1 through 3:

     0.136284112930298     0.148864336013794      0.13891065120697

 Columns 4 through 6:

     0.232420921325684    0.0961368083953857    0.0211357759595586





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