Thanks to all for your answers.
I took some time to think some more about the results and:
For the simple function (which returns 1), CPython roughly takes 1 sec
and pypy 13 secs. IMHO, this case reveals pypy's callback overhead.
For the complex function case, CPython roughly takes 6 secs and pypy 15
secs. If we assume that the overhead will be the same as in the simple
function case, and subtract it, then CPython roughly took 5 secs to
execute the complex function and pypy 2 secs.
From the above, i think that my jit guess was wrong (as Alex said), and
jit indeed works, but the overhead of the pypy callbacks is indeed quite
large.
I know that callbacks from C code aren't so frequent in Python programs,
but my project:
http://code.google.com/p/madis/
lives on them. So i would be glad if it could be solved (and madis's
data processing would become a lot faster).
Alternatively, is there something that i could do in my code that would
overcome this overhead? Like signalling to the jit engine that this
function should be always jitted and to do not waste time updating
statistics (or other tracking information) on it?
thanks
lefteris.
On 17/11/11 10:42, Antonio Cuni wrote:
On 11/17/2011 02:56 AM, Alex Gaynor wrote:
The JIT compiles functions without loops too now, so this should be jitted.
ctypes callbacks still go through the old _rawffi, so it's possible that this
introduces some unneeded overhead.
ciao,
Anto
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