[Tutor] timeit: 10million x 1 Vs 1million x 10
steve at pearwood.info
Thu Feb 28 11:36:50 CET 2013
On 28/02/13 13:27, DoanVietTrungAtGmail wrote:
> Dear tutors
> My function below simply populates a large dict. When measured by timeit
> populating 10 million items once, versus populating 1 million items ten
> times, the times are noticeably different:
I cannot replicate your results. When I try it, I get more or less the same
result each time:
py> for count, N in ((1, 10000000), (10, 1000000)):
... t = timeit.Timer('f(N)', 'from __main__ import N, writeDict as f')
... print min(t.repeat(number=count))
The difference is insignificant.
However, I did notice that when I ran your code, memory consumption went to
80% on my computer, and the load average exceeded 4. I suggest that perhaps
the results you are seeing have something to do with your operating system's
response to memory usage, or some other external factor.
> My guess is that this discrepancy is a result of either how some sort of
> overhead in timeit, or of Python having to allocate memory space for a dict
> 10 times. What do you think, and how to find out for sure?
Whatever the answer is, it is neither of the above.
Firstly, while timeit does have some overhead, it is very small. After all,
timeit is designed for timing tiny sub-microsecond code snippets.
You can get an idea of timeit's overhead like this:
py> from timeit import Timer
py> t1 = Timer("x = 2")
py> t2 = Timer("x = 1;x = 2")
If timeit had no overhead at all, t2 should take twice as long as t1 since
it has two instructions rather than one. But it doesn't, so we can calculate
the (approximate) overhead with a bit of maths:
overhead + t = 0.0486
overhead + 2t = 0.0690
Solving this gives me an overhead of 0.0282s, which is per the one million
loops that timeit does by default. So as you can see, it's quite small: about
30 nanoseconds on my computer per loop. Even if it was a million times
greater, it wouldn't be enough to explain the results you see.
As for your other suggestion, about the memory space allocation, it is also
unlikely to be correct. You are using the same dict on every test! On the
first run, Python has to reallocate memory to make the dict big enough for
10 million entries. After that, the dict is already resized and never gets
> Second (for me, this question is more important), how to improve
> performance? (I tried a tuple rather than a list for the dict values, it
> was slightly faster, but I need dict items to be mutable)
Firstly, are you sure you need to improve performance?
Secondly, performance of what? Have you profiled your application to see
which parts are slow, or are you just guessing?
As it stands, I cannot advise you how to speed your application up, because
I don't know what it does or what bits are slow. But I doubt very much that
the slow part is storing a small list inside a dict.
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