On 2/12/2009 12:20 PM, David Cournapeau wrote: > It does if you have access to the parallel toolbox I mentioned earlier > in this thread (again, no experience with it, but I think it is > specially popular on clusters; in that case, though, it is not limited > to thread-based implementation).
As has been mentioned, Matlab is a safer language for parallel computing as arrays are immutable. There is almost no need for synchronization of any sort, except barriers. Maybe it is time to implement an immutable ndarray subclass? With immutable arrays we can also avoid making temporary arrays in expressions like y = a*b + c. y just gets an expression and three immutable buffers. And then numexpr (or something like it) can take care of the rest. As for Matlab, I have noticed that they are experimenting with CUDA now, to use nvidia's processors for hardware-acceleration. As even modest GPUs can yield hundreds of gigaflops, that is going to be hard to match (unless we make an ndarray that uses the GPU). But again, as the performance of GPUs comes from massive multithreading, immutability may be the key here as well. Sturla Molden _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
