Optimizing list processing
Steven D'Aprano
steve+comp.lang.python at pearwood.info
Thu Dec 12 00:54:22 CET 2013
I have some code which produces a list from an iterable using at least
one temporary list, using a Decorate-Sort-Undecorate idiom. The algorithm
looks something like this (simplified):
table = sorted([(x, i) for i,x in enumerate(iterable)])
table = [i for x,i in table]
The problem here is that for large iterables, say 10 million items or so,
this is *painfully* slow, as my system has to page memory like mad to fit
two large lists into memory at once. So I came up with an in-place
version that saves (approximately) two-thirds of the memory needed.
table = [(x, i) for i,x in enumerate(iterable)]
table.sort()
for x, i in table:
table[i] = x
For giant iterables (ten million items), this version is a big
improvement, about three times faster than the list comp version. Since
we're talking about the difference between 4 seconds and 12 seconds (plus
an additional 40-80 seconds of general slow-down as the computer pages
memory into and out of virtual memory), this is a good, solid
optimization.
Except that for more reasonably sized iterables, it's a pessimization.
With one million items, the ratio is the other way around: the list comp
version is 2-3 times faster than the in-place version. For smaller lists,
the ratio varies, but the list comp version is typically around twice as
fast. A good example of trading memory for time.
So, ideally I'd like to write my code like this:
table = [(x, i) for i,x in enumerate(iterable)]
table.sort()
if len(table) < ?????:
table = [i for x,i in table]
else:
for x, i in table:
table[i] = x
where ????? no doubt will depend on how much memory is available in one
contiguous chunk.
Is there any way to determine which branch I should run, apart from hard-
coding some arbitrary and constant cut-off value?
--
Steven
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