marc magrans de abril wrote:
Hi,
I was a trying to profile a small script and after shrinking the code
to the minimum I got a interesting profile difference.
Given two test functions test1 and test2, that only differs from an
extra level of indirection (i.e. find_substr),
That's because there is a function call overhead.
I wonder why I got a
timming difference >50%?
A very simple function body will (in terms of percentage) have larger
function overhead. With a slightly more complex function body, the body
will takes much more time than the function call overhead.
What is the recommended way to factorize the
code? Should I write a big method containing everything?
Look in the absolute numbers: 0.666 CPU seconds vs. 0.248 CPU seconds
over 1000000 loops means if you put everything into one big method
you'll only save 418 nanoseconds per loop. Even over 1000000 loops; you
only save 0.418 seconds. Is it worth optimizing?
----Profiles test1
Profiles results:
1000003 function calls in 0.666 CPU seconds
----Profile test2:
3 function calls in 0.248 CPU seconds
I got a more striking difference: 5.291 CPU seconds vs 0.589 CPU
seconds. But knowing how the profiler works, this is to be expected.
Function call overhead become much (and I mean much) heavier with
profiler ON. I get a more sane result with timing manually:
import time
start = time.time()
test1(t)
print time.time() - start
start = time.time()
test2(t)
print time.time() - start
It's 1.186 vs 0.608, which is blink of an eye vs. blink of an eye.
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