Hi, Let's say that Numpy provides a GPU version on GPU. How would that work with all the packages that expect the memory to be allocated on CPU? It's not that Numpy refuses a GPU implementation, it's that it wouldn't solve the problem of GPU/CPU having different memory. When/if nVidia decides (finally) that memory should be also accessible from the CPU (like AMD APU), then this argument is actually void.
Matthieu 2018-01-02 22:21 GMT+01:00 Yasunori Endo <jo7...@gmail.com>: > Hi all > > Numba looks so nice library to try. > Thanks for the information. > > This suggests a new, higher-level data model which supports replicating >> data into different memory spaces (e.g. host and GPU). Then users (or some >> higher layer in the software stack) can dispatch operations to suitable >> implementations to minimize data movement. >> >> Given NumPy's current raw-pointer C API this seems difficult to >> implement, though, as it is very hard to track memory aliases. >> > I understood modifying numpy.ndarray for GPU is technically difficult. > > So my next primitive question is why NumPy doesn't offer > ndarray like interface (e.g. numpy.gpuarray)? > I wonder why everybody making *separate* library, making user confused. > Is there any policy that NumPy refuse standard GPU implementation? > > Thanks. > > > -- > Yasunori Endo > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > > -- Quantitative analyst, Ph.D. Blog: http://blog.audio-tk.com/ LinkedIn: http://www.linkedin.com/in/matthieubrucher
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