On Tue, Jun 28, 2016 at 10:36 PM, Michael Ward <mw...@cims.nyu.edu> wrote:
> Heya, I'm not a numbers guy, but I maintain servers for scientists and > researchers who are. Someone pointed out that our numpy installation on a > particular server was only using one core. I'm unaware of the who/how the > previous version of numpy/OpenBLAS were installed, so I installed them from > scratch, and confirmed that the users test code now runs on multiple cores > as expected, drastically increasing performance time. > > Now the user is writing back to say, "my test code is fast now, but > numpy.test() is still about three times slower than <some other server we > don't manage>". When I watch htop as numpy.test() executes, sure enough, > it's using one core. Now I'm not sure if that's the expected behavior or > not. Questions: > > * if numpy.test() is supposed to be using multiple cores, why isn't it, > when we've established with other test code that it's now using multiple > cores? > Some numpy.linalg functions (like np.dot) will be using multiple cores, but np.linalg.test() takes only ~1% of the time of the full test suite. Everything else will be running single core. So your observations are not surprising. Cheers, Ralf
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