Hi, The package compyte [1] contain a new gpu nd array that is in development. Currently there is indexing with int, slice and tuple with a mix of int and slice that are supported. Those create gpu nd array that have in some case not contiguous memory. No code in pycuda accept that, but you can make a copy of the gpu ndarray to use it with pycuda code. There is no direct conversion from a gpu nd array to pycuda gpuarray.
The package have a bad installation system. Look into the Makefile and change it to your platform. Currently we don't support numpy "advanced indexing" or list of int in the __getitem__ function. thanks Frédéric Bastien [1] https://github.com/inducer/compyte On Sun, May 29, 2011 at 9:54 AM, Andreas Kloeckner <li...@informa.tiker.net> wrote: > On Sun, 29 May 2011 16:08:28 +1000, <brett.br...@csiro.au> wrote: >> Just wondering if there are plans/timelines for implementing the Numpy >> multidimensional and fancy indexing/slicing in PyCUDA? We use this >> feature a lot to do repetitive data processing. There is not much of a >> performance improvement from indexing on CPU then passing to GPU, >> processing, then passing back. It seems to me that passing a single >> multidimensional array to GPU memory then indexing/slicing and >> processing entirely on the GPU would provide substantially greater >> performance improvement? > > This is being worked on--see the recent announcement regarding the > 'gpundarray' list. No precise time line, but potentially soon--faster if > you help. :) > > Andreas > > _______________________________________________ > PyCUDA mailing list > PyCUDA@tiker.net > http://lists.tiker.net/listinfo/pycuda > > _______________________________________________ PyCUDA mailing list PyCUDA@tiker.net http://lists.tiker.net/listinfo/pycuda
