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. Also there are computations where using multiple processors do not help. First, there is a certain overhead due to thread synchronization and scheduling the workload. Thus you want have a certain amount of work before you consider to invoke multiple threads. Seconds, hierachical memory also makes it mandatory to avoid that the threads share the same objects in cache. Otherwise the performance will degrade as more threads are added. A more technical answer is that NumPy's internals does not play very nicely with multithreading. For examples the array iterators used in ufuncs store an internal state. Multithreading would imply an excessive contention for this state, as well as induce false sharing of the iterator object. Therefore, a multithreaded NumPy would have performance problems due to synchronization as well as hierachical memory collisions. Adding multithreading support to the current NumPy core would just degrade the performance. NumPy will not be able to use multithreading efficiently unless we redesign the iterators in NumPy core. That is a massive undertaking which prbably means rewriting most of NumPy's core C code. A better strategy would be to monkey-patch some of the more common ufuncs with multithreaded versions. > I have never seen this happening with numpy except for the linalgebra > stuff (e.g lapack). > > Any comments? The BLAS/LAPACK library can use multithreading internally, depending on which BLAS/LAPACK library you use. Sturla _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
