Matthieu Brucher wrote:
Matlab surely relies on MKL to do this (Matlab ships with MKL or ACML
now). The latest Intel library handles multiprocessing, so if you want
to use multithreading, use MKL (and it can handle quad-cores with no
sweat). So Numpy is multithreaded.
I have AMD processor
MKL does the multithreading on its own for level 3 BLAS instructions
(OpenMP). For ACML, the problem is that AMD does not provide a CBLAS
interface and is not interested in doing so. With ACML, the compilation
fails with the current Numpy, but hopefully with Scons it will work, at
least
On 1/8/08, Matthieu Brucher [EMAIL PROTECTED] wrote:
I have AMD processor so I guess I should use ACML somehow instead.
However, at 1st I would prefer my code to be platform-independent, and
at 2nd unfortunately I haven't encountered in numpy documentation (in
website scipy.org and
Matthieu Brucher wrote:
MKL does the multithreading on its own for level 3 BLAS
instructions (OpenMP). For ACML, the problem is that AMD does
not provide a CBLAS interface and is not interested in doing
so. With ACML, the compilation fails with the current
As others have mentioned, the quickest and easiest way of getting
these things is to build numpy against a LAPACK/BLAS that has
threading support enabled. I have not played with this, but there is
no reason it shouldn't work out of the box.
On Jan 7, 2008 2:26 PM, dmitrey [EMAIL PROTECTED]
Yes, the problem in this implementation is that it uses pthreads for
synchronization instead of spin locks with a work pool implementation
tailored to numpy. The thread synchronization overhead is horrible
(300,000-400,000 clock cycles) and swamps anything other than very large
arrays. I
On Jan 8, 2008 3:33 AM, Matthieu Brucher [EMAIL PROTECTED] wrote:
I have AMD processor so I guess I should use ACML somehow instead.
However, at 1st I would prefer my code to be platform-independent, and
at 2nd unfortunately I haven't encountered in numpy documentation (in
website
Some days ago there was mentioned a parallel numpy that is developed by
Brian Granger.
Does the project have any blog or website? Has it any description about
API and abilities? When 1st release is intended?
Regards, D
___
Numpy-discussion mailing
Dmitrey,
This work is being funded by a new NASA grant that I have at Tech-X
Corporation where I work. The grant officially begins as of Jan 18th,
so not much has been done as of this point. We have however been
having some design discussions with various people.
Here is a broad overview of
The only one thing I'm very interested in for now - why the most
simplest matrix operations are not implemented to be parallel in numpy
yet (for several-CPU computers, like my AMD Athlon X2). First of all
it's related to matrix multiplication and devision, either point or
matrix (i.e. like
dmitrey wrote:
The only one thing I'm very interested in for now - why the most
simplest matrix operations are not implemented to be parallel in numpy
yet (for several-CPU computers, like my AMD Athlon X2). First of all
it's related to matrix multiplication and devision, either point or
2008/1/7, dmitrey [EMAIL PROTECTED]:
The only one thing I'm very interested in for now - why the most
simplest matrix operations are not implemented to be parallel in numpy
yet (for several-CPU computers, like my AMD Athlon X2). First of all
it's related to matrix multiplication and devision,
Robert Kern wrote:
dmitrey wrote:
The only one thing I'm very interested in for now - why the most
simplest matrix operations are not implemented to be parallel in numpy
yet (for several-CPU computers, like my AMD Athlon X2). First of all
it's related to matrix multiplication and
dmitrey wrote:
The only one thing I'm very interested in for now - why the most
simplest matrix operations are not implemented to be parallel in numpy
yet (for several-CPU computers, like my AMD Athlon X2).
For what it's worth, sometimes I *want* my numpy operations to happen
only on one
Andrew Straw wrote:
dmitrey wrote:
The only one thing I'm very interested in for now - why the most
simplest matrix operations are not implemented to be parallel in numpy
yet (for several-CPU computers, like my AMD Athlon X2).
For what it's worth, sometimes I *want* my numpy
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