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sum() several times slower on Python 3 64-bit #68264
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I got a report that summing numbers is noticably slower on Python 3. This is easily reproducible:
$ time python2.7 -c "print sum(xrange(3, 10**9, 3)) + sum(xrange(5, 10**9, 5)) - sum(xrange(15, 10**9, 15))" 233333333166666668
real 0m6.165s
user 0m6.100s
sys 0m0.032s$ time python3.4 -c "print(sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15)))" 233333333166666668
real 0m16.413s
user 0m16.086s
sys 0m0.089sI can't tell from initial poking what's the core issue here. Both examples produce equivalent bytecode, the builtin_sum() function is only noticably different in the fact that it uses PyLong_* across the board, including PyLong_AsLongAndOverlow. We'll need to profile this, which I didn't have time for yet.
Can't reproduce on 32-bit Linux.
$ time python2.7 -c "print sum(xrange(3, 10**9, 3)) + sum(xrange(5, 10**9, 5)) - sum(xrange(15, 10**9, 15))" 233333333166666668
real 1m11.614s
user 1m11.376s
sys 0m0.056s
$ time python3.4 -c "print(sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15)))"
233333333166666668real 1m11.658s
user 1m10.980s
sys 0m0.572s$ python2.7 -m timeit -n1 -r1 "sum(xrange(3, 10**9, 3)) + sum(xrange(5, 10**9, 5)) - sum(xrange(15, 10**9, 15))" 1 loops, best of 1: 72 sec per loop $ python3.4 -m timeit -n1 -r1 "sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15))" 1 loops, best of 1: 72.5 sec per loop $ python2.7 -m timeit -s "a = list(range(10**6))" -- "sum(a)" 10 loops, best of 3: 114 msec per loop $ python3.4 -m timeit -s "a = list(range(10**6))" -- "sum(a)" 10 loops, best of 3: 83.5 msec per loop
What is sys.int_info on your build?
I reproduce under 64-bit Linux. So this may be because the Python long digit (30 bits) is smaller than the C long (64 bits).
Lukasz: is there a specific use case? Note you can use Numpy for such calculations.
Serhiy, this is 64-bit specific. Antoine, as far as I can tell, the main use case is: "Don't make it look like migrating to Python 3 is a terrible performance downgrade."
As we discussed on the language summit this year [1], we have to be at least not worse to look appealing. This might be a flawed benchmark but people will make them anyway. In this particular case, there's internal usage at Twitter that unearthed it. The example is just a simplified repro.
Some perf degradations were expected, like switching text to Unicode. In this case, the end result computed by both 2.7 and 3.4 is the same so we should be able to address this.
If that's due to the different representation of Python 2's int type and Python 3's int type then I don't see an easy solution to this.
Łukasz: there are three ingredients here - sum, (x)range and the integer addition that sum will be performing at each iteration. Is there any chance you can separate the effects on your machine?
On my machine (OS X, 64-bit), I'm seeing *some* speed difference in the integer arithmetic, but not enough to explain the whole of the timing mismatch.
One thing we've lost in Python 3 is the fast path for small-int addition *inside* the ceval loop. It may be possible to restore something there.
Throwing out sum, I'm seeing significant slowdown simply from xrange versus range:
taniyama:Desktop mdickinson$ python2 -m timeit -s 'x = xrange(3, 10**9, 3)' 'for e in x: pass'
10 loops, best of 3: 5.01 sec per loop
taniyama:Desktop mdickinson$ python3 -m timeit -s 'x = range(3, 10**9, 3)' 'for e in x: pass'
10 loops, best of 3: 8.62 sec per loopthere are three ingredients here - sum, (x)range and the integer addition that sum will be performing at each iteration.
... not to forget the interpreter startup time on his machine. :)
I did a tiny bit of profiling and about 90% of the time seems to be spent creating and deallocating throw-away PyLong objects. My guess is that it simply lacks a free-list in _PyLong_New().
It seems we (like the benchmarks posted) are spending a whole lot of time on something that's probably not relevant to any real-world situation.
If someone has actual code that suffers from this, it would be good to know about it.
(note by the way that summing on a range() can be done O(1): it's just a variation on a arithmetic series)I don't think it's irrelevant. Throw-away integers are really not uncommon. For-loops use them quite often, non-trivial arithmetic expressions can create a lot of intermediate temporaries. Speeding up the create-delete cycle of PyLong sounds like a very obvious thing to do.
Imagine some code that iterates over a list of integers, applies some calculation to them, and then stores them in a new list, maybe even using a list comprehension or so. If you could speed up the intermediate calculation by avoiding overhead in creating temporary PyLong objects, such code could benefit a lot.
I suspect that adding a free-list for single-digit PyLong objects (the most common case) would provide some visible benefit.
Le 01/05/2015 08:09, Stefan Behnel a écrit :
I don't think it's irrelevant. Throw-away integers are really not
uncommon. For-loops use them quite often, non-trivial arithmetic
expressions can create a lot of intermediate temporaries. Speeding up
the create-delete cycle of PyLong sounds like a very obvious thing to do.That may be a good thing indeed. I'm just saying that the benchmarks
people are worried about here are completely pointless.I tried implementing a freelist. Patch attached, mostly adapted from the one in dictobject.c, but certainly needs a bit of cleanup.
The results are not bad, about 10-20% faster:
Original:
$ ./python -m timeit 'sum(range(1, 100000))' 1000 loops, best of 3: 1.86 msec per loop $ ./python -m timeit -s 'l = list(range(1000, 10000))' '[(i*2+5) // 7 for i in l]' 1000 loops, best of 3: 1.05 msec per loop
With freelist:
$ ./python -m timeit 'sum(range(1, 100000))' 1000 loops, best of 3: 1.52 msec per loop $ ./python -m timeit -s 'l = list(range(1000, 10000))' '[(i*2+5) // 7 for i in l]' 1000 loops, best of 3: 931 usec per loop
Antoine asked:
If someone has actual code that suffers from this, it would be good to know about it.
You might have missed Łukasz' earlier comment: "In this particular case, there's internal usage at Twitter that unearthed it. The example is just a simplified repro."
31 remaining items
3.11
D:\python311>python Python 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] on win32 >>> import time >>> t=time.time();sum(range(1,pow(10,8)+1));print(time.time()-t) 5000000050000000 4.157237768173218
vs
3.10D:\python310>python Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug 1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)] on win32 >>> import time >>> t=time.time();sum(range(1,pow(10,8)+1));print(time.time()-t) 5000000050000000 4.183239221572876
pypy 7.3.9
D:\pypy3.8-v7.3.9-win64>pypy Python 3.8.12 (0089b4a7ab2306925a251b35912885d52ead1aba, Mar 16 2022, 13:51:04) [PyPy 7.3.9 with MSC v.1929 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>>> import time >>>> t=time.time();sum(range(1,pow(10,8)+1));print(time.time()-t) 5000000050000000 0.1780109405517578
This can be closed. On 3.14:
$ time python3.14 -c "print(sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15)))" 233333333166666668 real 0m3.706s user 0m3.687s sys 0m0.005sMuch faster than @ambv benchmarks.
real 0m6.165s
user 0m6.100s
sys 0m0.032sreal 0m16.413s
user 0m16.086s
sys 0m0.089sDiscussion of further optimization belongs elsewhere (faster-cpython).
This can be closed. On 3.14:
I'm not sure. Absolute numbers in benchmarks aren't relevant here. OP probably run py2 tests on a different system than you.
At least, you should run test for py2 and py3 on same system. Here are my tests.
Py2:
$ python2.7 -m timeit -n1 -r1 "sum(xrange(3, 10**9, 3)) + sum(xrange(5, 10**9, 5)) - sum(xrange(15, 10**9, 15))" 1 loops, best of 1: 9.85 sec per loopPy3.13:
$ python3.13 -m timeit -n1 -r1 "sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15))" 1 loop, best of 1: 39.9 sec per loopPy3.14a7:
$ python3.14 -m timeit -n1 -r1 "sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15))" 1 loop, best of 1: 22.3 sec per loopFor me it looks like issue is valid and it's a regression from py2, not a feature request. @picnixz ? @ambv ?
Honestly, considering 2.7 has been EOL for a long time, I don't think we need to keep this specific issue open. I don't think we can do anything now and I would indeed prefer if faster-cpython comes up with a solution.
Now, for performance loss, we sometimes treat them as bug, sometimes not. I categorized it as a FR because we won't backport the change I think.
Honestly, considering 2.7 has been EOL for a long time, I don't think we need to keep this specific issue open.
Why not? If v2.7 is better somewhere - it's still a regression.
I don't think we can do anything now and I would indeed prefer if faster-cpython comes up with a solution.
I'm not sure that closing issue is a right thing even if we can't do anything now. Work is ongoing, e.g.: https://discuss.python.org/t/87950. I think it's a good thing to keep eye on this issue for people involved.
BTW, Py3.14 has impressive speedup, but I doubt it's related to integer arithmetic.
Reacted by Guido van RossumI'm with @skirpichev. Can someone at least summarize an explanation of the difference between 2.7 and 3.x?
I think it was analyzed by #68264 (comment) but AFAICT, it's the call to
PyLong_AsLongAndOverflowthat introduced the regression.Work is ongoing, e.g.: discuss.python.org/t/87950
I wasn't aware of this one so thanks. By the way, feel free to re-open issues if I close them wrongly!
Can someone at least summarize an explanation of the difference between 2.7 and 3.x?
My 2c:
- one issue was mentioned by @mdickinson: sum() several times slower on Python 3 64-bit #68264 (comment) and gone ~3.11: sum() several times slower on Python 3 64-bit #68264 (comment)
- second one was missing specialization for single-digit integers (in a loop): sum() several times slower on Python 3 64-bit #68264 (comment). This was added by GH-101291: Rearrange the size bits in PyLongObject #102464.
- something happened on 3.14 (as my benchmarks suggests). I don't know yet.
Here more tests from my zoo:
$ python2.7 -m timeit -n1 -r1 "sum(xrange(3, 10**9, 3)) + sum(xrange(5, 10**9, 5)) - sum(xrange(15, 10**9, 15))" 1 loops, best of 1: 9.85 sec per loop $ python3.9 -m timeit -n1 -r1 "sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15))" 1 loop, best of 1: 27.7 sec per loop $ python3.10 -m timeit -n1 -r1 "sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15))" 1 loop, best of 1: 28.4 sec per loop $ python3.11 -m timeit -n1 -r1 "sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15))" 1 loop, best of 1: 26.1 sec per loop $ python3.12 -m timeit -n1 -r1 "sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15))" 1 loop, best of 1: 31.3 sec per loop $ python3.13 -m timeit -n1 -r1 "sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15))" 1 loop, best of 1: 33.1 sec per loop $ python3.14 -m timeit -n1 -r1 "sum(range(3, 10**9, 3)) + sum(range(5, 10**9, 5)) - sum(range(15, 10**9, 15))" 1 loop, best of 1: 21.8 sec per loopAFACIT, what changed between 3.13 and main (not 3.14, I haven't built 3.14 locally) is the time to iterate over:
$ python3.13 -m timeit -n1 -r1 -s 'r1 = range(3,10**9,3)' 'for _ in r1: pass' 1 loop, best of 1: 2.2 sec per loop $ python3.15 -m timeit -n1 -r1 -s 'r1 = range(3,10**9,3)' 'for _ in r1: pass' 1 loop, best of 1: 1.57 sec per loop
We're roughly 30% faster between 3.13 and 3.15 just for iterations, which is roughly the gain between 3.13 and 3.14 for the benchmark above. I don't have a 2.7 that I can test but one possibility is that it's not just sum() that is impacted.
Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.
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