Note also that OpenBlas claims performance as good as MKL with Sandy Bridge processors.
https://github.com/xianyi/OpenBLAS/wiki/faq#wiki-sandybridge_perf George Nurser. On 10 November 2012 00:38, Dag Sverre Seljebotn <[email protected]>wrote: > On 11/09/2012 11:57 PM, Matthieu Brucher wrote: > > Hi, > > > > A.A slower than A.A' is not a surprise for me. The latter is far more > > cache friendly that the former. Everything follows cache lines, so it is > > faster than something that will use one element from each cache line. In > > fact it is exactly what "proves" that the new version is correct. > > Good job (if all the tests were made and still pass ;) ) > > Cache lines shouldn't matter much with a decent BLAS? > > http://dl.acm.org/citation.cfm?id=1356053 > > (Googling for "Anatomy of High-Performance Matrix Multiplication" will > give you a preprint outside of paywall, but Google appears to not want > to give me the URL of a too long search result so I can't paste it). > > Dag Sverre > > > > > Cheers, > > > > Matthieu > > > > > > 2012/11/9 Nicolas SCHEFFER <[email protected] > > <mailto:[email protected]>> > > > > Ok: comparing apples to apples. I'm clueless on my observations and > > would need input from you guys. > > > > Using ATLAS 3.10, numpy with and without my changes, I'm getting > these > > timings and comparisons. > > > > # > > #I. Generate matrices using regular dot: > > # > > big = np.array(np.random.randn(2000, 2000), 'f'); > > np.savez('out', big=big, none=big.dot(big), both=big.T.dot(big.T), > > left=big.T.dot(big), right=big.dot(big.T))" > > > > # > > #II. Timings with regular dot > > # > > In [3]: %timeit np.dot(big, big) > > 10 loops, best of 3: 138 ms per loop > > > > In [4]: %timeit np.dot(big, big.T) > > 10 loops, best of 3: 166 ms per loop > > > > In [5]: %timeit np.dot(big.T, big.T) > > 10 loops, best of 3: 193 ms per loop > > > > In [6]: %timeit np.dot(big.T, big) > > 10 loops, best of 3: 165 ms per loop > > > > # > > #III. I load these arrays and time again with the "fast" dot > > # > > In [21]: %timeit np.dot(big, big) > > 10 loops, best of 3: 138 ms per loop > > > > In [22]: %timeit np.dot(big.T, big) > > 10 loops, best of 3: 104 ms per loop > > > > In [23]: %timeit np.dot(big.T, big.T) > > 10 loops, best of 3: 138 ms per loop > > > > In [24]: %timeit np.dot(big, big.T) > > 10 loops, best of 3: 102 ms per loop > > > > 1. A'.A': great! > > 2. A.A' becomes faster than A.A !?! > > > > # > > #IV. MSE on differences > > # > > In [25]: np.sqrt(((arr['none'] - none)**2).sum()) > > Out[25]: 0.0 > > > > In [26]: np.sqrt(((arr['both'] - both)**2).sum()) > > Out[26]: 0.0 > > > > In [27]: np.sqrt(((arr['left'] - left)**2).sum()) > > Out[27]: 0.015900515 > > > > In [28]: np.sqrt(((arr['right'] - right)**2).sum()) > > Out[28]: 0.015331409 > > > > # > > # CCl > > # > > While the MSE are small, I'm wondering whether: > > - It's a bug: it should be exactly the same > > - It's a feature: BLAS is taking shortcuts when you have A.A'. The > > difference is not significant. Quick: PR that asap! > > > > I don't have enough expertise to answer that... > > > > Thanks much! > > > > -nicolas > > On Fri, Nov 9, 2012 at 2:13 PM, Nicolas SCHEFFER > > <[email protected] <mailto:[email protected]>> > wrote: > > > I too encourage users to use scipy.linalg for speed and robustness > > > (hence calling this scipy.dot), but it just brings so much > confusion! > > > When using the scipy + numpy ecosystem, you'd almost want > everything > > > be done with scipy so that you get the best implementation in all > > > cases: scipy.zeros(), scipy.array(), scipy.dot(), > scipy.linalg.inv(). > > > > > > Anyway this is indeed for another thread, the confusion we'd like > to > > > fix here is that users shouldn't have to understand the C/F > > contiguous > > > concepts to get the maximum speed for np.dot() > > > > > > To summarize: > > > - The python snippet I posted is still valid and can speed up your > > > code if you can change all your dot() calls. > > > - The change in dotblas.c is a bit more problematic because it's > very > > > core. I'm having issues right now to replicate the timings, I've > got > > > better timing for a.dot(a.T) than for a.dot(a). There might be a > bug. > > > > > > It's a pain to test because I cannot do the test in a single > > python session. > > > I'm going to try to integrate most of your suggestions, I cannot > > > guarantee I'll have time to do them all though. > > > > > > -nicolas > > > On Fri, Nov 9, 2012 at 8:56 AM, Nathaniel Smith <[email protected] > > <mailto:[email protected]>> wrote: > > >> On Fri, Nov 9, 2012 at 4:25 PM, Gael Varoquaux > > >> <[email protected] > > <mailto:[email protected]>> wrote: > > >>> On Fri, Nov 09, 2012 at 03:12:42PM +0000, Nathaniel Smith wrote: > > >>>> But what if someone compiles numpy against an optimized blas > (mkl, > > >>>> say) and then compiles SciPy against the reference blas? What > > do you > > >>>> do then!? ;-) > > >>> > > >>> This could happen. But the converse happens very often. What > > happens is > > >>> that users (eg on shared computing resource) ask for a > > scientific python > > >>> environment. The administrator than installs the package > > starting from > > >>> the most basic one, to the most advanced one, thus starting > > with numpy > > >>> that can very well build without any external blas. When he > > gets to scipy > > >>> he hits the problem that the build system does not detect > > properly the > > >>> blas, and he solves that problem. > > >>> > > >>> Also, it used to be that on the major linux distributions, > > numpy would not > > >>> be build with an optimize lapack because numpy was in the > > 'base' set of > > >>> packages, but not lapack. On the contrary, scipy being in the > > 'contrib' > > >>> set, it could depend on lapack. I just checked, and this has > > been fixed > > >>> in the major distributions (Fedora, Debian, Ubuntu). > > >>> > > >>> Now we can discuss with such problems should not happen, and > > put the > > >>> blame on the users/administrators, the fact is that they happen > > often. I > > >>> keep seeing environments in which np.linalg is unreasonnably > slow. > > >> > > >> If this is something that's been a problem for you, maybe we > should > > >> start another thread on things we could do to fix it directly? > > Improve > > >> build instructions, advertise build systems that set up the whole > > >> environment (and thus do the right thing), make numpy's setup.py > > >> scream and yell if blas isn't available...? > > >> > > >> -n > > >> _______________________________________________ > > >> NumPy-Discussion mailing list > > >> [email protected] <mailto:[email protected]> > > >> http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > > NumPy-Discussion mailing list > > [email protected] <mailto:[email protected]> > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > > > > > -- > > Information System Engineer, Ph.D. > > Blog: http://matt.eifelle.com > > LinkedIn: http://www.linkedin.com/in/matthieubrucher > > Music band: http://liliejay.com/ > > > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > [email protected] > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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