Thu, 10 Feb 2011 20:49:28 +0000, Pauli Virtanen wrote: [clip] > 1. Check first if the bottleneck is in the inner reduction loop > (function DOUBLE_add in loops.c.src:712) or in the outer iteration > (function PyUFunc_ReductionOp in ufunc_object.c:2781). > 2. If it's in the inner loop, some optimizations are possible, e.g. > specialized cases for sizeof(item) strides. Think how to add them > cleanly.
A quick check (just replace the inner loop with a no-op) shows that for 100 items, the bottleneck is in the inner loop. The cross-over between inner loop time and strided iterator overhead apparently occurs around ~20-30 items (on the machine I used for testing). Anyway, spending time for optimizing the inner loop for a 30% speed gain (max) seems questionable... -- Pauli Virtanen _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
