Tim:
> A lot of things get mixed up here ;-) The _mean_ is actually useful
> if you're using a poor-resolution timer with a fast test.
In which case discrete probability distributions are better than my assumption
of a continuous distribution.
I looked at the distribution of times for 1,000 repeats of
t1 = time.time()
t2 = time.time()
times.append(t2-t1)
The times and counts I found were
9.53674316406e-07 388
1.19209289551e-06 95
1.90734863281e-06 312
2.14576721191e-06 201
2.86102294922e-06 2
1.90734863281e-05 1
3.00407409668e-05 1
This implies my Mac's time.time() has a resolution of 2.3841857910000015e-07 s
(0.2µs or about 4.2MHz.) Or possibily a small integer fraction thereof. The
timer overhead takes between 4 and 9 ticks. Ignoring the outliers, assuming I
have the CPU all to my benchmark for the timeslice then I expect about +/- 3
ticks of noise per test.
To measure 1% speedup reliably I'll need to run, what, 300-600 ticks? That's
a millisecond, and with a time quantum of 10 ms there's a 1 in 10 chance that
I'll incur that overhead.
In other words, I don't think my high-resolution timer is high enough. Got
a spare Cray I can use, and will you pay for the power bill?
Andrew
dalke at dalkescientific.com
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