On Wed, Sep 9, 2009 at 10:41 AM, Francesc Alted <fal...@pytables.org> wrote: >> Numexpr mainly supports functions that are meant to be used element-wise, >> so the operation/element ratio is normally 1 (or close to 1). In these >> scenarios is where improved memory access is much more important than CPU >> (or, for that matter, GPU), and is the reason why numexpr is much more >> efficient than NumPy when evaluating complex expressions like >> ``a*b+c*sqrt(d)``. >> >> In other words, a GPU-enabled numexpr makes little sense.
There's another way of looking at this, which has been mentioned before in the conversation, but which I think should be mentioned again... The cost of transfer to and from a GPU is very high, compared with most of the sorts of things that we do with ndarrays. So the approach of using libraries to speed up little pieces here and there (i.e. with VML or ATLAS) but basically to let stock numpy take care of the rest does not work. In order to benefit from huge speedups on a GPU, data need to be on the GPU already. It is a good idea to perform low-instruction density functions on the GPU even when the CPU could go just as fast (or even if the CPU is faster!) just to ensure that the data stay on the GPU. Suppose you want to evaluate "dot(a*b+c*sqrt(d), e)". The GPU is great for doing dot(), but if you have to copy the result of the elemwise expression to the GPU before you can start doing dot(), then the performance advantage is ruined. Except for huge matrices, you might as well just leave the data in the system RAM and use a normal BLAS library. So that's why it is a good idea to use the GPU to do some functions even when the CPU would be faster for them (in isolation). All that said, there is a possibility that future devices (and some laptops already?) will use an integrated memory system that might make 'copying to the GPU' a non-issue... but we're not there yet I think... James -- http://www-etud.iro.umontreal.ca/~bergstrj _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
