Nathaniel Smith <n...@pobox.com> writes:
> Such optimizations are important enough that numpy operations always
> give the option of explicitly specifying the output array (like
> in-place operators but more general and with clumsier syntax). Here's
> an example small-array benchmark that IIUC uses Jacobi iteration to
> solve Laplace's equation. It's been written in both natural and
> hand-optimized formats (compare "num_update" to "num_inplace"):
>
> https://yarikoptic.github.io/numpy-vbench/vb_vb_app.html#laplace-inplace
>
> num_inplace is totally unreadable, but because we've manually elided
> temporaries, it's 10-15% faster than num_update.
Does it really have to be that ugly? Shouldn't using
tmp += u[2:,1:-1]
tmp *= dy2
instead of
np.add(tmp, u[2:,1:-1], out=tmp)
np.multiply(tmp, dy2, out=tmp)
give the same performance? (yes, not as nice as what you're proposing,
but I'm still curious).
Best,
-Nikolaus
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