On Thu, Feb 10, 2011 at 8:29 AM, eat <e.antero.ta...@gmail.com> wrote:
> Hi, > > Observing following performance: > In []: m= 1e5 > In []: n= 1e2 > In []: o= ones(n) > In []: M= randn(m, n) > In []: timeit M.sum(1) > 10 loops, best of 3: 38.3 ms per loop > In []: timeit dot(M, o) > 10 loops, best of 3: 21.1 ms per loop > > In []: m= 1e2 > In []: n= 1e5 > In []: o= ones(n) > In []: M= randn(m, n) > In []: timeit M.sum(1) > 100 loops, best of 3: 18.3 ms per loop > In []: timeit dot(M, o) > 10 loops, best of 3: 21.2 ms per loop > > One would expect sum to outperform dot with a clear marginal. Does there > exixts any 'tricks' to increase the performance of sum? > > I'm not surprised, much depends on the version of ATLAS or MKL you are linked to. If you aren't linked to either and just using numpy's version then the results are a bit strange. With numpy development I get In [1]: m= 1e5 In [2]: n= 1e2 In [3]: o= ones(n) In [4]: M= randn(m, n) In [5]: timeit M.sum(1) 100 loops, best of 3: 19.2 ms per loop In [6]: timeit dot(M, o) 100 loops, best of 3: 15 ms per loop In [7]: m= 1e2 In [8]: n= 1e5 In [9]: o= ones(n) In [10]: M= randn(m, n) In [11]: timeit M.sum(1) 100 loops, best of 3: 17.4 ms per loop In [12]: timeit dot(M, o) 100 loops, best of 3: 14.2 ms per loop Chuck
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