On Mi, 2016-06-29 at 02:03 -0700, Nathaniel Smith wrote: > As a general rule I wouldn't worry too much about test speed. Speed > is > extremely dependent on exact workloads. And this is doubly so for > test > suites -- production workloads tend to do a small number of normal > things over and over, while a good test suite never does the same > thing twice and spends most of its time exercising weird edge > conditions. So unless your actual workload is running the numpy test > suite :-), it's probably not worth trying to track down. >
Agreed, the test suit, and likely also the few tests which might take most time in the end, could be arbitrarily weird and skewed. I could for example imagine IO speed being a big factor. Also depending on system configuration (or numpy version) a different number of tests may be run sometimes. What might make somewhat more sense would be to compare some of the benchmarks `python runtests.py --bench` if you have airspeed velocity installed. While not extensive, a lot of those things at least do test more typical use cases. Though in any case I think the user should probably just test some other thing. - Sebastian > And yeah, numpy does not in general do automatic multithreading -- > the > only automatic multithreading you should see is when using linear > algebra functions (matrix multiply, eigenvalue calculations, etc.) > that dispatch to the BLAS. > > -n > > On Wed, Jun 29, 2016 at 12:07 AM, Ralf Gommers <ralf.gomm...@gmail.co > m> wrote: > > > > > > > > On Wed, Jun 29, 2016 at 3:27 AM, Chris Barker - NOAA Federal > > <chris.bar...@noaa.gov> wrote: > > > > > > > > > > > > > > > > > > > > > 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 > > > > > > > > > > > * 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. > > > > > > > > > Though why it would run slower on one box than another comparable > > > box is a > > > mystery... > > > > Maybe just hardware config? I see a similar difference between how > > long the > > test suite runs on TravisCI vs my linux desktop (the latter is > > slower, > > surprisingly). > > > > Ralf > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > >
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