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<div class="moz-cite-prefix">On 23/03/2013 12:05 AM, Chris Barker -
NOAA Federal wrote:<br>
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<pre wrap="">On Fri, Mar 22, 2013 at 2:39 PM, Colin J. Williams
<a class="moz-txt-link-rfc2396E" href="mailto:cjwilliams43@gmail.com"><cjwilliams43@gmail.com></a> wrote:
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<pre wrap="">I have updated to numpy 1.7.0 for each of the Pythons 2.7.3, 3.2.3 and
3.3.0.
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<pre wrap="">...
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<pre wrap="">The tests, which are available
here(<a class="moz-txt-link-freetext" href="http://web.ncf.ca/cjw/FP%20Summary%20over%20273-323-330.txt">http://web.ncf.ca/cjw/FP%20Summary%20over%20273-323-330.txt</a>), show that
3.2 is slower, but not to the same degree reported before.
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Have posted your test code anywhere? Anyway, depending on how you did
your timings, that looks to me like 3.* is a bit faster with small
data, and pretty much within measurement error for the large datasets.
And if the large ones are doing things with really big arrays (I'm
assuming pretty big, as you're getting close to 32 bit memory
limits...), then it's really hard to imagine how python version could
make a noticeable difference -- the real work would be in the numpy
code, and that's exactly the same on all python versions.
If you are using BLAS or LAPACK stuff, then there might be some
differences with the different builds, though I wouldn't expect so if
you ar getting them from the same source.
-Chris
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<big><big>I used the versions from PyPi, this choice has been
questioned. I'll compare with the SourceForge versions. Also,
I shall be incorporating the random SEED.<br>
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I expect to report the results in the next week or so.<br>
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The test code used is available here: <a
href="http://web.ncf.ca/cjw/testFPSpeed.py">http://web.ncf.ca/cjw/testFPSpeed.py</a><br>
<br>
Colin W.<br>
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