Fix Python – Why does multiprocessing use only a single core after I import numpy?

Question

Asked By – ali_m

I am not sure whether this counts more as an OS issue, but I thought I would ask here in case anyone has some insight from the Python end of things.

I’ve been trying to parallelise a CPU-heavy for loop using joblib, but I find that instead of each worker process being assigned to a different core, I end up with all of them being assigned to the same core and no performance gain.

Here’s a very trivial example…

from joblib import Parallel,delayed
import numpy as np

def testfunc(data):
    # some very boneheaded CPU work
    for nn in xrange(1000):
        for ii in data[0,:]:
            for jj in data[1,:]:
                ii*jj

def run(niter=10):
    data = (np.random.randn(2,100) for ii in xrange(niter))
    pool = Parallel(n_jobs=-1,verbose=1,pre_dispatch='all')
    results = pool(delayed(testfunc)(dd) for dd in data)

if __name__ == '__main__':
    run()

…and here’s what I see in htop while this script is running:

htop

I’m running Ubuntu 12.10 (3.5.0-26) on a laptop with 4 cores. Clearly joblib.Parallel is spawning separate processes for the different workers, but is there any way that I can make these processes execute on different cores?

Now we will see solution for issue: Why does multiprocessing use only a single core after I import numpy?


Answer

After some more googling I found the answer here.

It turns out that certain Python modules (numpy, scipy, tables, pandas, skimage…) mess with core affinity on import. As far as I can tell, this problem seems to be specifically caused by them linking against multithreaded OpenBLAS libraries.

A workaround is to reset the task affinity using

os.system("taskset -p 0xff %d" % os.getpid())

With this line pasted in after the module imports, my example now runs on all cores:

htop_workaround

My experience so far has been that this doesn’t seem to have any negative effect on numpy‘s performance, although this is probably machine- and task-specific .

Update:

There are also two ways to disable the CPU affinity-resetting behaviour of OpenBLAS itself. At run-time you can use the environment variable OPENBLAS_MAIN_FREE (or GOTOBLAS_MAIN_FREE), for example

OPENBLAS_MAIN_FREE=1 python myscript.py

Or alternatively, if you’re compiling OpenBLAS from source you can permanently disable it at build-time by editing the Makefile.rule to contain the line

NO_AFFINITY=1

This question is answered By – ali_m

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