Mikkel Krøigård <[EMAIL PROTECTED]> writes:
> Well, there are many more or less interesting conclusions to draw
> from your benchmark, Martin. Not surprisingly, matrix multiplication
> turns out to be expensive.
Hmm... I did see that there were a bunch of calls to __mul__ in
matrix.py, but I thought they came from the initialization of _hyper
in ActiveRuntime. The the initialization of _hyper does not use any
matrix multiplications, so this is wrong?!
When the mul method uses prss_get_triple, then _hyper should never be
used or even initialized and so there should be no matrix stuff going
on... I think I measured the wrong code somehow :-)
> One thing I really do find interesting about the table is the amount
> of time spent in inc_pc_wrapper. Perhaps it is possible to improve
> this somehow?
It only used 0.5 seconds of its own time -- the 21 seconds are the
total time spend in the child-calls made by inc_pc_wrapper. Since it
wraps all important functions its clear that the cumulative time will
be big:
ncalls tottime percall cumtime percall
48003/6003 0.518 0.000 21.195 0.004
But any optimization would be good -- if we can same a tiny bit for
each of the 48000 calls it might sum up :-)
--
Martin Geisler
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