"Qiu, Wenning" <Wenning_Qiu@[...].com>  writes:

>  My project will be running on an SMP box and requires
>  scalability. However, my test shows that Python threading has very
>  poor performance in terms of scaling. In fact it doesn't scale at all.
> 
>  I wrote a simple test program to complete given number of iterations
>  of a simple loop. The total number of iterations can be divided evenly
>  among a number of threads. My test shows that as the number of threads
>  grows, the CPU usage grows and the response time gets longer. For
>  example, to complete the same amount of work, one thread takes 10
>  seconds, 2 threads take 20 seconds and 3 threads take 30 seconds.
> 
>  The fundamental reason for lacking scalability is that Python uses a
>  global interpreter lock for thread safety. That global lock must be
>  held by a thread before it can safely access Python objects.
> 
>  I thought I might be able to make embedded Python scalable by
>  embedding multiple interpreters and have them run independently in
>  different threads. However "Python/C API Reference Manual" chapter 8
>  says that "The global interpreter lock is also shared by all threads,
>  regardless of to which interpreter they belong". Therefore with
>  current implementation, even multiple interpreters do not provide
>  scalability.
> 
>  Has anyone on this list run into the same problem that I have, or does
>  anyone know of any plan of totally insulating multiple embedded Python

This question has little to do with Boost.Python, so I doubt you'll
find good answers here.  I would try comp.lang.python; I think people
will rush to help you there.

Good luck,
-- 
Dave Abrahams
Boost Consulting
www.boost-consulting.com


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