On Mon, Mar 8, 2010 at 11:53 AM, Maciej Fijalkowski <fij...@gmail.com> wrote:
> Hello.
>
> Half a year ago, I was asking about sympy benchmarks. So here is some
> feedback on what I got using the JIT. Running the very simple
> benchmark, like this:
>
> import sympy
> import time
> x, y = sympy.symbols('x, y')
>
> for i in range(10):
> t0 = time.time()
> sympy.factor(x**20 - y**20)
> print time.time() - t0
>
> (I run it 10 times for JIT to warm up)
>
> I get the following results:
>
> CPython 2.6.4:
>
> 0.894039154053
> 0.738809108734
> 0.749840021133
> 0.817865848541
> 0.72897195816
> 0.771667957306
> 0.777043104172
> 0.770677089691
> 0.770140886307
> 0.877714157104
>
> PyPy trunk (rev 71774):
>
> 1.19711208344
> 0.634068012238
> 0.557782888412
> 0.614212036133
> 0.521821022034
> 0.577347993851
> 0.481374979019
> 0.466244935989
> 0.444444894791
> 0.520822048187
>
> So overal we get some (not too much) speedup, after warmup, without
> even profiling that case. I suppose we can do much better if we try
> slightly harder.
Also note that sympy uses caching, so you might try it with caching turned off:
http://wiki.sympy.org/wiki/FAQ#How_do_I_turn_off_caching.3F
>
> What do you think? Is it an interesting direction to pursue? Are
> people interested?
I am interested. What kind of things can pypy compile in time using JIT?
Ondrej
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