Unexpected performance curve from CPython merge sort
Posted
by
vkazanov
on Stack Overflow
See other posts from Stack Overflow
or by vkazanov
Published on 2012-04-03T16:41:06Z
Indexed on
2012/04/04
5:29 UTC
Read the original article
Hit count: 322
I have implemented a naive merge sorting algorithm in Python. Algorithm and test code is below:
import time
import random
import matplotlib.pyplot as plt
import math
from collections import deque
def sort(unsorted):
if len(unsorted) <= 1:
return unsorted
to_merge = deque(deque([elem]) for elem in unsorted)
while len(to_merge) > 1:
left = to_merge.popleft()
right = to_merge.popleft()
to_merge.append(merge(left, right))
return to_merge.pop()
def merge(left, right):
result = deque()
while left or right:
if left and right:
elem = left.popleft() if left[0] > right[0] else right.popleft()
elif not left and right:
elem = right.popleft()
elif not right and left:
elem = left.popleft()
result.append(elem)
return result
LOOP_COUNT = 100
START_N = 1
END_N = 1000
def test(fun, test_data):
start = time.clock()
for _ in xrange(LOOP_COUNT):
fun(test_data)
return time.clock() - start
def run_test():
timings, elem_nums = [], []
test_data = random.sample(xrange(100000), END_N)
for i in xrange(START_N, END_N):
loop_test_data = test_data[:i]
elapsed = test(sort, loop_test_data)
timings.append(elapsed)
elem_nums.append(len(loop_test_data))
print "%f s --- %d elems" % (elapsed, len(loop_test_data))
plt.plot(elem_nums, timings)
plt.show()
run_test()
As much as I can see everything is OK and I should get a nice N*logN curve as a result. But the picture differs a bit:
Things I've tried to investigate the issue:
- PyPy. The curve is ok.
- Disabled the GC using the gc module. Wrong guess. Debug output showed that it doesn't even run until the end of the test.
- Memory profiling using meliae - nothing special or suspicious. ` I had another implementation (a recursive one using the same merge function), it acts the similar way. The more full test cycles I create - the more "jumps" there are in the curve.
So how can this behaviour be explained and - hopefully - fixed?
UPD: changed lists to collections.deque
UPD2: added the full test code
UPD3: I use Python 2.7.1 on a Ubuntu 11.04 OS, using a quad-core 2Hz notebook. I tried to turn of most of all other processes: the number of spikes went down but at least one of them was still there.
© Stack Overflow or respective owner