On Wed, May 7, 2014 at 7:11 PM, Sturla Molden <sturla.mol...@gmail.com> wrote: > On 03/05/14 23:56, Siegfried Gonzi wrote: > > I noticed IDL uses at least 400% (4 processors or cores) out of the box > > for simple things like reading and processing files, calculating the > > mean etc. > > The DMA controller is working at its own pace, regardless of what the > CPU is doing. You cannot get data faster off the disk by burning the > CPU. If you are seeing 100 % CPU usage while doing file i/o there is > something very bad going on. If you did this to an i/o intensive server > it would go up in a ball of smoke... The purpose of high-performance > asynchronous i/o systems such as epoll, kqueue, IOCP is actually to keep > the CPU usage to a minimum.
That said, reading data stored in text files is usually a CPU-bound operation, and if someone wrote the code to make numpy's text file readers multithreaded, and did so in a maintainable way, then we'd probably accept the patch. The only reason this hasn't happened is that no-one's done it. -n -- Nathaniel J. Smith Postdoctoral researcher - Informatics - University of Edinburgh http://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion