[SciPy-Dev] scipy improve performance by parallelizing

[Sai Rajeshwar]

hi , thanks for suggestions

  actually iam running my code on stampede tacc. where numpy,scipy are
built against mkl libraries for optimal performance.. observations are as
follows
--------------------------------------------------

 1) setting different OMP_NUM_THREADS to different values didnot change the
runtimes
2)the code took same time as it took on mac pro with accelerated framework
for blas and lapack..

so is mkl not being helpful, or its not getting configured to use
multithreads

--------------------------
the statements taking lot fo time are like folllows
--------------------

1)  for i in xrange(conv_out_shape[1]):

conv_out[0][i]=scipy.signal.convolve(self.input[0][i%self.image_shape[1]],numpy.rot90(self.W[0][i/self.image_shape[1]],2),mode='valid')



2)for i in xrange(pooled_shape[1]):
            for j in xrange(pooled_shape[2]):
                for k in xrange(pooled_shape[3]):
                    for l in xrange(pooled_shape[4]):

pooled[0][i][j][k][l]=math.tanh((numpy.sum(conv_out[0][i][j][k*3][l*3:(l+1)*3])+numpy.sum(conv_out[0][i][j][k*3+1][l*3:(l+1)*3])+numpy.sum(conv_out[0][i][j][k*3+2][l*3:(l+1)*3]))/9.0+b[i][j])


thanks

*with regards..*

*M. Sai Rajeswar*
*M-tech  Computer Technology*


*IIT Delhi----------------------------------Cogito Ergo Sum---------*


On Fri, Jul 11, 2014 at 6:02 PM, Derek Homeier <
derek at astro.physik.uni-goettingen.de> wrote:

> On 10 Jul 2014, at 05:19 pm, Ashwin Srinath <ashwinsrnth at gmail.com> wrote:
>
> > I'm no expert, so I'll just share a few links to start this discussion.
> You definitely want to look at Cython if you're computing with NumPy
> arrays. If you're familiar with the MPI programming model, you want to
> check out mpi4py. If you have NVIDIA GPUs that you'd like to take advantage
> of, check out PyCUDA.
> >
> > Thanks,
> > Ashwin
> >
> >
> > On Thu, Jul 10, 2014 at 6:08 AM, Sai Rajeshwar <rajsai24 at gmail.com>
> wrote:
> > hi all,
> >
> >    im trying to optimise a python code takes huge amount of time on
> scipy functions such as scipy.signa.conv. Following are some of my queries
> regarding the same.. It would be great to hear from you..  thanks..
> > ----------------------------------------------------
> >   1) Can Scipy take advantage of multi-cores.. if so how
> > 2)what are ways we can improve the performance of scipy/numpy functions
> eg: using openmp, mpi etc
> > 3)If scipy internally use blas/mkl libraries can we enable parallelism
> through these?
> >
> If your operations are using the BLAS functions a lot, you get SMP
> parallelisation for very cheap by
> linking to the multithreaded MKL or ACML versions and setting
> OMP_NUM_THREADS/MKL_NUM_THREADS
> to the no. of available cores.
>
> Cheers,
>                                                         Derek
>
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