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
