Hey Toni,
If this optimization is valid for any float, we should definitely do it,
and this is a missed optimization. If it's not valid for all floats, I'm
not sure how we should handle it, if at all.
Alex
On Wed Nov 05 2014 at 10:16:36 AM Toni Mattis <
toni.mat...@student.hpi.uni-potsdam.de> wrote:
> Hello,
>
> I discovered that PyPy's JIT generates "DIVSD" instructions on xmm
> registers when dividing a float by a constant C. This consumes an order
> of magnitude more CPU cycles than the corresponding "MULSD" instruction
> with a precomputed 1/C.
>
> I know that only powers of two have an exact reciprocal floating point
> representation, but there might be a benefit in trading the least
> significant digit for a more significant speedup.
>
> So, is this a missed optimization (at least for reasonably accurate
> cases), a present or possibly future option (like -ffast-math in gcc) or
> are there more reasons against it?
>
>
> Thanks,
>
> Toni
>
>
> --- PS: Small Example ---
>
> This function takes on average 0.41 seconds to compute on an
> array.array('d') with 10**8 elements between 0 and 1:
>
> def spikes_div(data, threshold=1.99):
> count = 0
> for i in data:
> if i / 0.5 > threshold:
> count += 1
> return count
>
> Rewritten with a multiplication it takes about 0.29 seconds on average,
> speeding it up by factor 1.4:
>
> ...
> if i * 2.0 > threshold:
> ...
>
>
> The traces contain the same instructions (except for the MULSD/DIVSD)
> and run the same number of times. I'm working with a fresh translation
> of the current PyPy default on Ubuntu 14.04 x64 with a 2nd generation
> Core i7 CPU.
>
>
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