Single core problem-dependant performances ? about scs HOT 3 OPEN

@bodono just to confirm: SCS + OMP_NUM_THREADS + MKL works for us in the julia land ;)

from scs.

There are five places where multi-threading can speed up SCS:

1. In the matrix multiplies, primarily for the indirect solver but also to some small extent the direct solver - if compiled with USE_OPENMP = 1 .
2. In the projection onto the exponential cone - if compiled with USE_OPENMP = 1.
3. In the projection onto the semidefinite cone - if your lapack library is multi-threaded.
4. In the Anderson acceleration - if your lapack library is multi-threaded.
5. If using MKL then multiple threads will be used for the linear system solver and for the SD cone project and Anderson acceleration.

To control the number of threads you can use OMP_NUM_THREADS environment variable (I think this should also work for the MKL threads).

from scs.

I tried it out on a fairly large dataset, things end up being slightly slower with MKL than without. I guess for this kind of problem it would make more sense to run each solve on a different core with different data ?
Either way, thanks for the help !

Just as a note, if I install MKL with pip, it isn't recognized by setup.py when building from source with the --mkl flag. Installing with conda works just fine. This is on Ubuntu 20.04 LTS with Python 3.10.

from scs.