On Tue, Aug 18, 2009 at 1:12 AM, Fredrik
Johansson<fredrik.johans...@gmail.com> wrote:
>
> On Tue, Aug 18, 2009 at 8:50 AM, Maciej Fijalkowski<fij...@gmail.com> wrote:
>>
>> Hi.
>>
>>>
>>> I think SymPy is an excellent benchmark target. The nature of SymPy
>>> (or any computer algebra system) is such that any high-level operation
>>> will exercise most parts of the system. For example
>>> "integrate(x**3*exp(x)*sin(x), x)" performs ~4 million function calls
>>> to some 200 functions all over SymPy, and it's a calculation that
>>> you'd use SymPy for in practice, so it would be a good real-world test
>>> case.
>>>
>>> Also, mpmath might be a good target (mpmath is a subpackage of SymPy).
>>> There are some microbenchmarks at [1] although I could come up with
>>> some slightly more complex "real world" calculation if you are
>>> interested. Mpmath heavily depends on long integer performance in
>>> particular, but if you use low precision, it will exercise general
>>> Python performance. For myself, I would be interested in whether
>>> PyPy's new JIT can beat psyco, which all around makes mpmath ~2x
>>> faster on top of CPython.
>>
>> Long integer performance is not *exactly* on top of my list of stuff to look 
>> to.
>> About PyPy JIT beating psyco, yes, but not exactly right now :-)
>>
>> I was also wondering what *does not* exercise most of the system and yet
>> still makes some sort of sense.
>
> By wondering, do you mean that you are looking for this (i.e. you are
> looking for a benchmark that invokes a relatively small amount of
> code), or are you implicating me as not making sense? :-)
>

Heh :-) I suppose I'm looking for a benchmark that invokes relatively
small amount
of code, but still a bit more than a single loop.

Cheers,
fijal

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