Robert Kern wrote: > On Sat, Mar 15, 2008 at 2:48 PM, Gnata Xavier <[EMAIL PROTECTED]> wrote: >> Hi, >> >> Numpy is great : I can see several IDL/matlab projects switching to numpy :) >> However, it would be soooo nice to be able to put some OpenMP into the >> numpy code. >> >> It would be nice to be able to be able to use several CPU using the >> numpy syntax ie A=sqrt(B). >> >> Ok, we can use some inline C/C++ code but it is not so easy. >> Ok, we can split the data over several python executables (one per CPU) >> but A=sqrt(B) is so simple... >> >> numpy + recent gcc with OpenMP --> :) ? >> Any comments ? > > Eric Jones tried to use multithreading to split the computation of > ufuncs across CPUs. Ultimately, the overhead of locking and unlocking > made it prohibitive for medium-sized arrays and only somewhat > disappointing improvements in performance for quite large arrays. I'm > not familiar enough with OpenMP to determine if this result would be > applicable to it. If you would like to try, we can certainly give you > pointers as to where to start.
Perhaps I'm missing something. How is locking and synchronization an issue when each thread is writing to a mutually exclusive part of the output buffer? Thanks, Damian _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
