Hi Florin,
On Wed, 27 Jul 2016, Papa, Florin wrote:
The table contains run time values, normalized to the CPython Numpy
results. This means that a value of 1 is equal to the CPython NumPy
result, less than 1 means faster than CPython NumPy and more than 1 is
slower than CPython NumPy.
Thank you for the explanation!
I think this supports my assessment though, as I can't see how your
conclusion can be justified on the basis of this table:
"NumPyPy performance seems to be significantly slower compared to CPython
NumPy or even PyPy NumPy"
In fact, NumPyPy performance seems to be significantly *faster* compared
to CPython NumPy and, in any case, PyPy NumPy (with the exception of a few
benchmarks, such as "cauchy", which should be investigated).
I'd also be very curious as to whether you've tried the vectorizer
already, or these results were obtained without it.
--
Sincerely yours,
Yury V. Zaytsev
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