I switched to using numpy for the matrix multiply and while the
overall time to do the matrix multiply is much faster, there is still
no speed up from using more than one python thread. If I look at top
while running 2 or more threads, both cores are being used 100% and
there is no idle time on the system.
I did a quick google search and didn't find anything conclusive about
numpy releasing the GIL. The most conclusive and recent reference I
found was
http://mail.python.org/pipermail/python-list/2007-October/463148.html
I found some other references where people were expressing concern
over numpy releasing the GIL due to the fact that other C extensions
could call numpy and unexpectedly have the GIL released on them (or
something like that).
On May 15, 2008, at 6:43 PM, Nick Coghlan wrote:
Tom Pinckney wrote:
All the discussion recently about pyprocessing got me interested in
actually benchmarking Python's multiprocessing performance to see
if reality matched my expectations around what would scale up and
what would not. I knew Python threads wouldn't be good for compute
bound problems, but I was curious to see how well they worked for i/
o bound problems. The short answer is that for i/o bound problems,
python threads worked just as well as using multiple operating
system processes.
Interesting - given that your example compute bound problem happened
to be a matrix multiply, I'd be curious what the results are when
using python threads with numpy to do the same thing (my
understanding is that numpy will usually release the GIL while doing
serious number-crunching)
Cheers,
Nick.
--
Nick Coghlan | [EMAIL PROTECTED] | Brisbane, Australia
---------------------------------------------------------------
http://www.boredomandlaziness.org
_______________________________________________
Python-Dev mailing list
Python-Dev@python.org
http://mail.python.org/mailman/listinfo/python-dev
Unsubscribe:
http://mail.python.org/mailman/options/python-dev/archive%40mail-archive.com
