Pairwise summation
Numpy uses pairwise summation along the fast axis if that axis contains no more than 8192 elements. How was 8192 chosen? Doubling to 16384 would result in a lot more function call overhead due to the recursion. Is it a speed issue? Memory? Or something else entirely?
On Mon, 2019-01-07 at 12:15 -0800, Keith Goodman wrote:
Numpy uses pairwise summation along the fast axis if that axis contains no more than 8192 elements. How was 8192 chosen?
It is simply a constant used throughout the ufunc machinery (and iteration) for cache friendliness. However, that iteration should not always chunk to 8192 elements, it should often just the whole array. And I do not think the inner loop has anything chunking itself, so given a contiguous fast axis and no casting, you likely already get a single outer iteration. In any case 8192 chosen to be small enough to be cache friendly and is exposed as `np.BUFSIZE`, you can actually the buffer that is being used with `numpy.setbufsize(size)`, although I can't say I ever tried it. Note that it has to fit also larger datatypes and multiple buffers. - Sebastian
On Mon, 2019-01-07 at 12:15 -0800, Keith Goodman wrote:
Numpy uses pairwise summation along the fast axis if that axis contains no more than 8192 elements. How was 8192 chosen?
It is simply a constant used throughout the ufunc machinery (and iteration) for cache friendliness. However, that iteration should not always chunk to 8192 elements, it should often just the whole array. And I do not think the inner loop has anything chunking itself, so given a contiguous fast axis and no casting, you likely already get a single outer iteration. In any case 8192 chosen to be small enough to be cache friendly and is exposed as `np.BUFSIZE`, you can actually the buffer that is being used with `numpy.setbufsize(size)`, although I can't say I ever tried it. Note that it has to fit also larger datatypes and multiple buffers. - Sebastian
participants (2)
-
Keith Goodman -
Sebastian Berg