Hey guys,
On 08.05.13 04:31, Bin Huang wrote:
On Tue, May 7, 2013 at 6:54 PM, Richard Tew <[email protected]> wrote:
On Wed, May 8, 2013 at 8:17 AM, Bin Huang <[email protected]> wrote:
Hi Richard,
So I was able to compile with STACKLESS_OFF and manual removal of
'static'. I measured the performance again:
Problem size CPython Stackless Stackless_off
256x256 14.106 ms 39.331 ms 39.394 ms
512x512 110.648 ms 346.857 ms 350.049 ms
1024x1024 1022.090 ms 8949.712 ms 8926.275 ms
2048x2048 7795.782 ms 80161.503 ms 78647.046 ms
Then I obtained the source code for official Python 2.7.2 tarball
release and repeated the
process. Freshly installed Python 2.7.2 also showed slowdown. Clearly,
it is how
to install stackless Python and Numpy that matters.
Okay, if I understand you correctly, you tested official Python 2.7.2
tarball, and it was similar to the numbers for Stackless_off? The
official Python 2.7.2 tarball is not the cpython column, right?
That's correct. The CPython column I listed was the default Python installation
on my machine. And in fact its version is 2.7.3.
If this is the case, that any straight download and compilation of the
Python source code (whether Stackless or not) gives you similar slow
numbers, then there's something special about whatever gave the
numbers for cpython.
Is there anything special that you think I need to pay attention to?
Yes, the Stackless_off column. Stackless_off is official Python. It
completely compiles out the "Stackless patch" and should give
something that behaves exactly like the official Python with the same
version. So, your official tarball compile should give you the same
numbers as Stackless_off.
I like it. I can see that the switch "stackless_off" makes debugging like this
much easier.
Thanks!
No, thank you for going to all this work. I expect your cpython is
either 2.7.3, and if so, you should get comparable numbers from the
2.7.3 stackless source code. Or it is installed from the packaging
system for your operating system and it compiles as 64 bit or
something different. Unless you are going to tell me that I
misunderstood, and that your cpython column was for the official 2.7.2
tarball, I do not think your problem lies with Stackless.
You just made a good point. So I went ahead and downloaded an official
2.7.3 tarball and repeated the same process. Guess what? Same slowdown
happened to official 2.7.3 tarball.
Hope this helps.
It really helps. Now I believe the problem is related to how I installed Numpy.
I actually had two Numpy installations. One was installed by packaging system
(which has superior performance) and the other was by myself manually.
It is time to bother Numpy community :-).
That was a very interesting conversation!
Please let me know what the crucial problem with Numpy was.
Is that maybe related to continuum's Numba, maybe even Jit code?
cheers - chris
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