A Thursday 10 September 2009 11:20:21 Gael Varoquaux escrigué: > On Thu, Sep 10, 2009 at 10:36:27AM +0200, Francesc Alted wrote: > > Where are you getting this info from? IMO the technology of memory in > > graphics boards cannot be so different than in commercial > > motherboards. It could be a *bit* faster (at the expenses of packing less > > of it), but I'd say not as much as 4x faster (100 GB/s vs 25 GB/s of > > Intel i7 in sequential access), as you are suggesting. Maybe this is GPU > > cache bandwidth? > > I believe this is simply because the transfers is made in parallel to the > different processing units of the graphic card. So we are back to > importance of embarrassingly parallel problems and specifying things with > high-level operations rather than for loop.
Sure. Specially because NumPy is all about embarrasingly parallel problems (after all, this is how an ufunc works, doing operations element-by-element). The point is: are GPUs prepared to compete with a general-purpose CPUs in all- road operations, like evaluating transcendental functions, conditionals all of this with a rich set of data types? I would like to believe that this is the case, but I don't think so (at least not yet). -- Francesc Alted
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