Re: Help me understand why PyPy is so slow in my benchmark

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Hello Jeremy,

ouch. it turns out the problem is neither your code, nor PyPy, but
timeit. The timeit module is just not a safe benchmarking tool unless
you use it in *exactly* the right way. The way that timeit runs code is
using an exec'ed string, eg f"recurse({i})" in your case. This
bytecode-compiles some code for every time you call timeit.repeat. This
means that the JIT has to compile that bytecode to machine code for
every call to repeat. That is the overhead that you were measuring.

So your code:

	for i in range(1, 4000):
		times["iter"][i] =3D repeat(
			stmt =3D f"iterate({i})",
			...

simply never warms up.

To fix this, you could write your own timing helper. I did that (I'm
attaching my version of the script).

My results (timing all the 4000 numbers together, per implementation)
are like this:

CPython 3.11.6:
iter 1.976924208
rcsv 2.300890293

PyPy3 7.3.17:
iter 0.265697421 (7.4x faster)
rcsv 0.643804392 (3.6x faster)

I also tried to use a list and "".join, a bytearray, and a pypy-specific
stringbuilder, all were slower. Yes, your string additions are
quadratic. But the longest string that is being built is 15, and doing
1+2+3+...+15=3D107 characters copies is still very cheap.

My main takeaway would be that micro-benchmarking is very hard :-(.

Would it be ok if I use your code in a small PSA blog post about timeit?

Cheers,

CF



On 2024-09-20 04:16, Jeremy Brown wrote:
> Manuel,
>
>> Your code is more similar to the code example that shows a case where
>> the optimization doesn=E2=80=99t work.
>>
>> Does that explain the behavior you=E2=80=99re seeing? Feel free to make
>> suggestions on how to clarify the wording.
>
> I think I just interpreted that statement incorrectly, I thought the
> linear code section explained how the JIT optimized the code, not the
> preconditions needed for the JIT to function correctly.
>
> CF,
>
>> Hi Jeremy,
>>
>> Can you share how you are running this function? I tried a few variants=
, and pypy is often faster than CPython on my attempts, so the rest of you=
r code is necessary to find out what's wrong.
>
> Sure, here's a link to a copy of the benchmark: https://pastebin.com/fR0=
C6qcB.
>
> I also know the JIT definitely can't optimize the iterative version,
> but even if this is a problem specific to PyPy I wouldn't expect runs
> to take twice as long compared to CPython.
>
> -Jeremy
> _______________________________________________
> pypy-dev mailing list -- [email protected]
> To unsubscribe send an email to [email protected]
> https://mail.python.org/mailman3/lists/pypy-dev.python.org/
> Member address: [email protected]

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