Hi all, I am curious about exploring whether or not we could add simple blocked iteration to NumPy. It seems like a long standing small deficiency in NumPy that we do not support blocked iteration. I do not know how much speed gain we would actually have in real world code, but I assume some bad-memory-order copies could be drastically faster.
Implementing blocked iteration for NumPy seems pretty complicated on first sight due to the complexity of the iterator and the fact that almost no-one knows the code well or touches it regularly. But, the actual complexity to add a new iteration mode to it is probably not forbiddingly high. First, we need to (quickly) find the cases where blocked iteration makes sense, and then, if it does, store whatever additional metadata is necessary. Second, we need to provide a newly implemented `iternext` function. The first chunk, seems like it can be done in its own function and should be fairly straight forward to do after most of the iterator setup is done. While the second part is already how the iterator is designed. It would be helpful to have someone with some expertise/brackground in this type of thing to be able discuss trade-offs and quickly see what the main goals should be and whether/where significant performance increases are likely. There are some things that may end up being complicated. For example, if we would want to support reductions/broadcasting. However, it may well be that there is no reason to attack those more complex cases, because the largest gains are expected elsewhere in any case. I would be extremely happy if anyone with the necessary background is interested in giving this challenge a try. I can help with the NumPy- API side, and code review and would be available for chatting/helping with the NumPy side. I do not have the bandwidth to actually dive into this for real though. Cheers, Sebastian [1] That is the complexity concerning the NumPy API. I do not know how complex a blocked iterator itself is.
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