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... Thanks a lot, James. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
