for simple convolutions there is also np.convolve compared to scipy it releases the GIL and you can use normal python threads for parallization if you need to compute many independent convolutions and not just one. That said scipy should probably release the GIL too, probably a bug that it doesn't. On 10.07.2014 17:19, Ashwin Srinath wrote:
Hey, Sai
I'm no expert, so I'll just share a few links to start this discussion. You definitely want to look at Cython <http://cython.org/> if you're computing with NumPy arrays. If you're familiar with the MPI programming model, you want to check out mpi4py <http://mpi4py.scipy.org/>. If you have NVIDIA GPUs that you'd like to take advantage of, check out PyCUDA <http://mathema.tician.de/software/pycuda/>.
Thanks, Ashwin
On Thu, Jul 10, 2014 at 6:08 AM, Sai Rajeshwar <rajsai24@gmail.com <mailto:rajsai24@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?
looks like i have to work on internals of scipy.. thanks a lot..
*with regards..* * * *M. Sai Rajeswar* *M-tech Computer Technology* *IIT Delhi ----------------------------------Cogito Ergo Sum--------- *
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