Dear Keldor, On Mon, 8 Aug 2011 23:32:12 -0600, <[email protected]> wrote: > I’m trying to divide a very large image (larger than device memory) into some > number of slices for processing. The problem is that the image may or may > not divide evenly into N slices, meaning that I either have to deal with > slices of differing sizes, which complicates image coordinates even more than > they already are, or else use some sort of padding. > > Padding seems to be the best solution, however there is a nasty problem. > Since there’s no way of copying a small array into a larger array (just copy > the small array into the beginning of the larger array, and leave the > remaining part, the padding, untouched), or of copying the first part of a > larger array back into the small array (the rest of the array is, again, > padding and not needed), I have to either create an intermediate padded > staging array and do extra copying, or else pad the entire image array with > numpy.resize, which is horribly slow and memory consuming for images of this > size. > > What I really need is a partial array to partial array copy mechanism. I > know that certain *limitations* of opencl 1.0 make this impossible with full > generality due to the lack of an offset into the device buffer, but even with > the device side offset fixed to 0, the byte_count is sufficient for what I > need to do, since the padding can always be placed at the end of the slice. > > I really need byte_count for a buffer<=>host transfer!
I didn't include byte_count because I think you can always achieve the same effect by obtaining a view of the numpy array you're copying. I.e. enqueue_copy(dev_buf, my_array, byte_count=16384) would be equivalent to enqueue_copy(dev_buf, my_array.flatten[:16384/my_array.dtype.itemsize]) Since both '.flatten' and view creation are O(1) operations (i.e. they don't copy data at all), this should do what you want, unless I'm misunderstanding you. (If so, please clarify.) Thanks, Andreas
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