Hi there,
I have some scientific application written in python. There is a
good deal of list processing, but also some "simple" computation such
as basic linear algebra involved. I would like to speed things up
implementing some of the functions in C. So I need profiling.
I first tried to use the default python profiler, but profiling my
application multiplies the execution time by a factor between 10 and
100 ! So I decided to give a try to hotspot. I just followed the
example of the python library reference, but I have some strange
results concerning cpu time. My profiling script is something like the
following:
def run_foo():
print time.clock()
function_to_profile()
print time.clock()
prof = hotshot.Profile("essai.prof")
benchtime= prof.runcall(run_foo)
prof.close()
stats = hotshot.stats.load("essai.prof")
stats.strip_dirs()
stats.sort_stats('time', 'calls')
stats.print_stats(20)
The goal is to profile the function function_to_profile(). Running this
script gives me a CPU executime time of around 2 seconds, whereas the
difference between the two clock calls is around 10 seconds ! And I
don't run any other cpu consuming tasks at the same time, so this
cannot come from other running processes. Is there something perticular
about hotspot timing I should know ? I am not sure how I can get more
accurate results with hotspot.
I would appreciate any help,
Thanks
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