On Mon, Jun 15, 2009 at 01:22, Bryan Cole<br...@cole.uklinux.net> wrote: > On Sun, 2009-06-14 at 15:50 -0500, Robert Kern wrote: >> On Sun, Jun 14, 2009 at 14:31, Bryan Cole<br...@cole.uklinux.net> wrote: >> > I'm starting work on an application involving cpu-intensive data >> > processing using a quad-core PC. I've not worked with multi-core systems >> > previously and I'm wondering what is the best way to utilise the >> > hardware when working with numpy arrays. I think I'm going to use the >> > multiprocessing package, but what's the best way to pass arrays between >> > processes? >> > >> > I'm unsure of the relative merits of pipes vs shared mem. Unfortunately, >> > I don't have access to the quad-core machine to benchmark stuff right >> > now. Any advice would be appreciated. >> >> You can see a previous discussion on scipy-user in February titled >> "shared memory machines" about using arrays backed by shared memory >> with multiprocessing. Particularly this message: >> >> http://mail.scipy.org/pipermail/scipy-user/2009-February/019935.html >> > > Thanks. > > Does Sturla's extension have any advantages over using a > multiprocessing.sharedctypes.RawArray accessed as a numpy view?
It will be easier to write code that correctly holds and releases the shared memory with Sturla's extension. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
