using python interpreters per thread in C++ program
sturlamolden at yahoo.no
Mon Sep 7 15:10:19 CEST 2009
On 7 Sep, 14:50, MRAB <pyt... at mrabarnett.plus.com> wrote:
> CPython's GIL means that multithreading on multiple processors/cores has
> limitations. Each interpreter has its own GIL, so processor-intensive
> applications work better using the multiprocessing module than with the
> threading module.
We incur a 200x speed-penalty from Python if the code is CPU-bound.
How much do you gain from one extra processor? Start by adding in some
C, and you will gain much more performance, even without parallel
Processor-intensive code should therefore use extension libraries that
release the GIL. Then you have option of getting parallel concurrency
by Python threads or OpenMP in C/Fortran. Most processor-intensive
code depend on numerical libraries written in C or Fortran anyway
(NumPy, ATLAS, LAPACK, FFTW, GSL, MKL, etc.) When you do this, the GIL
does not get in your way. The GIL is infact an advantage if one
library happen not to be thread safe. Many old Fortran 77 libraries
have functions with re-entrancy issues, due to SAVE attribute on
variables. Thus, the GIL is often an advantage for processor-intensive
multiprocessing is fragile and unreliable, btw.
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