numarray and SMP

Christopher T King squirrel at WPI.EDU
Thu Jul 1 19:09:21 CEST 2004

In a quest to speed up numarray computations, I tried writing a 'threaded 
array' class for use on SMP systems that would distribute its workload 
across the processors. I hit a snag when I found out that since the Python 
interpreter is not reentrant, this effectively disables parallel 
processing in Python. I've come up with two solutions to this problem, 
both involving numarray's C functions that perform the actual vector 

1) Surround the C vector operations with Py_BEGIN_ALLOW_THREADS and 
   Py_END_ALLOW_THREADS, thus allowing the vector operations (which don't 
   access Python structures) to run in parallel with the interpreter.
   Python glue code would take care of threading and locking.

2) Move the parallelization into the C vector functions themselves. This 
   would likely get poorer performance (a chain of vector operations
   couldn't be combined into one threaded operation).

I'd much rather do #1, but will playing around with the interpreter state 
like that cause any problems?

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