On 17/04/07, James Turner <[EMAIL PROTECTED]> wrote: > Hi Anne, > > Your reply to Lou raises a naive follow-up question of my own... > > > Normally, python's multithreading is effectively cooperative, because > > the interpreter's data structures are all stored under the same lock, > > so only one thread can be executing python bytecode at a time. > > However, many of numpy's vectorized functions release the lock while > > running, so on a multiprocessor or multicore machine you can have > > several cores at once running vectorized code. > > Are you saying that numpy's vectorized functions will perform a single > array operation in parallel on a multi-processor machine, or just that > the user can explicitly write threaded code to run *multiple* array > operations on different processors at the same time? I hope that's not > too stupid a question, but I haven't done any threaded programming yet > and the answer could be rather useful...
For the most part, numpy's vectorized functions don't do anything fancy in terms of computations; just giant for loops. What they do do (and not necessarily all of them) is release the GIL so another thread can be doing something else while they do that. That said, some of them (dot for example) use BLAS in certain situations, and then all bets are off. At the least a decent BLAS implementation will be smart about cache behaviour; a fancy BLAS implementation might actually vectorize the operation automatically. That would be using SSE3, though, or some vector processor (Cray?), not likely SMP. Though I can't say for sure. The scipy linear algebra functions use LAPACK, which is more likely to be able to make such speedups (and in fact I'm pretty sure there is an MPI-based LAPACK, though whether it's a plug-in replacement I don't know). Anne _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
