for loop in python is 10x slower than matlab

Question

I run python 2.7 and matlab R2010a on the same machine, doing nothing, and it gives me 10x different in speed

I looked online, and heard it should be the same order. Python will further slow down as if statement and math operator in the for loop

My question: is this the reality? or there is some other way let them in the same speed order?

Here is python code

import time

start_time = time.time()

for r in xrange(1000):

      for c in xrange(1000):

         continue

elapsed_time = time.time() - start_time

print 'time cost = ',elapsed_time

Output: time cost = 0.0377440452576

Here is matlab code

tic

for i = 1:1000

    for j = 1:1000

    end

end

toc

Output: Escaped time is 0.004200 seconds

I am not too if there is any difference to the compiler optimizer, but did you try pass instead of continue? Maybe if you add a very basic calculation, t = t+1 then it would change. — slbass
Testing the loop with python -m timeit shows that using pass is a bit slower than continue, but not in a significant way. On my machine the loops take 16 msec, which is less than half of what the OP claims. Also note that MATLAB has a JIT, hence such differences are to be expected especially with simple loops. — Bakuriu
Might be worth putting something inside the loop, just in case Matlab is clever enough to realize the loops don't actually do anything, and has optimized them out. — Aya
@Bakuriu Thanks for posting that link. I had not read it but it was very informative. I have updated my answer to address that. — slbass

slbass slbass · Accepted Answer · 2013-06-21T19:29:46

The reason this is happening is related to the JIT compiler, which is optimizing the MATLAB for loop. You can disable/enable the JIT accelerator using feature accel off and feature accel on. When you disable the accelerator, the times change dramatically.

MATLAB with accel on: Elapsed time is 0.009407 seconds.

MATLAB with accel off: Elapsed time is 0.287955 seconds.

python: time cost = 0.0511920452118

Thus the JIT accelerator is directly causing the speedup that you are noticing. There is another thing that you should consider, which is related to the way that you defined the iteration indices. In both cases, MATLAB and python, you used Iterators to define your loops. In MATLAB you create the actual values by adding the square brackets ([]), and in python you use range instead of xrange. When you make these changes

% MATLAB
for i = [1:1000]
    for j = [1:1000]

# python
for r in range(1000):
  for c in range(1000):

The times become

MATLAB with accel on: Elapsed time is 0.338701 seconds.

MATLAB with accel off: Elapsed time is 0.289220 seconds.

python: time cost = 0.0606048107147

One final consideration is if you were to add a quick computation to the loop. ie t=t+1. Then the times become

MATLAB with accel on: Elapsed time is 1.340830 seconds.

MATLAB with accel off: Elapsed time is 0.905956 seconds. (Yes off was faster)

python: time cost = 0.147221088409

I think that the moral here is that the computation speeds of for loops, out-of-the box, are comparable for extremely simple loops, depending on the situation. However, there are other, numerical tools in python which can speed things up significantly, numpy and PyPy have been brought up so far.

for loop in python is 10x slower than matlab

4 Answers