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Message: 1
Date: Wed, 07 May 2014 20:11:13 +0200
From: Sturla Molden sturla.mol...@gmail.com
Subject: Re: [Numpy-discussion] IDL vs Python parallel computing
To: numpy-discussion@scipy.org
Message-ID: lkdt01$jrc$1...@ger.gmane.org
On 08.05.2014 02:48, Frédéric Bastien wrote:
Just a quick question/possibility.
What about just parallelizing ufunc with only 1 inputs that is c or
fortran contiguous like trigonometric function? Is there a fast path in
the ufunc mechanism when the input is fortran/c contig? If that is the
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Message: 2
Date: Wed, 7 May 2014 19:25:32 +0100
From: Nathaniel Smith n...@pobox.com
Subject: Re: [Numpy-discussion] IDL vs Python parallel computing
To: Discussion of Numerical Python numpy-discussion@scipy.org
Message-ID:
CAPJVwBnqMnrKo0=tthln1pvepwov_rh
On 05/05/14 17:02, Francesc Alted wrote:
Well, this might be because it is the place where using several
processes makes more sense. Normally, when you are reading files, the
bottleneck is the I/O subsystem (at least if you don't have to convert
from text to numbers), and for calculating the
On 03/05/14 23:56, Siegfried Gonzi wrote:
I noticed IDL uses at least 400% (4 processors or cores) out of the box
for simple things like reading and processing files, calculating the
mean etc.
The DMA controller is working at its own pace, regardless of what the
CPU is doing. You cannot
On Wed, May 7, 2014 at 7:11 PM, Sturla Molden sturla.mol...@gmail.com wrote:
On 03/05/14 23:56, Siegfried Gonzi wrote:
I noticed IDL uses at least 400% (4 processors or cores) out of the box
for simple things like reading and processing files, calculating the
mean etc.
The DMA
On 07.05.2014 20:11, Sturla Molden wrote:
On 03/05/14 23:56, Siegfried Gonzi wrote:
A more technical answer is that NumPy's internals does not play very
nicely with multithreading. For examples the array iterators used in
ufuncs store an internal state. Multithreading would imply an
Just a quick question/possibility.
What about just parallelizing ufunc with only 1 inputs that is c or fortran
contiguous like trigonometric function? Is there a fast path in the ufunc
mechanism when the input is fortran/c contig? If that is the case, it would
be relatively easy to add an openmp
On 5/3/14, 11:56 PM, Siegfried Gonzi wrote:
Hi all
I noticed IDL uses at least 400% (4 processors or cores) out of the box
for simple things like reading and processing files, calculating the
mean etc.
I have never seen this happening with numpy except for the linalgebra
stuff (e.g
Hi all
I noticed IDL uses at least 400% (4 processors or cores) out of the box
for simple things like reading and processing files, calculating the
mean etc.
I have never seen this happening with numpy except for the linalgebra
stuff (e.g lapack).
Any comments?
Thanks,
Siegfried
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