[Python-Dev] Python parallel benchmark

[Tom Pinckney]

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.

I wrote two simple benchmarks, one compute bound and the other i/o  
bound. The compute bound one did a parallel matrix multiply and the i/ 
o bound one read random records from a remote MySQL database. I ran  
each benchmark via python's thread module and via MPI (using mpi4py  
and openmpi and Send()/Recv() for communication). Each test was run  
multiple times and the numbers were consistent between test runs. I  
ran the tests on a dual-core Macbook Pro running OS X 10.5 and the  
included python 2.5.1.

1) Python threads

a) compute bound:

1 thread - 16 seconds
2 threads - 21 seconds

b) i/o bound:

1 thread -- 13 seconds
4 threads -- 10 seconds
8 threads -- 5 seconds
12 threads - 4 seconds

2) MPI

a) compute bound:

1 thread - 17 seconds
2 threads -- 11 seconds

b) i/o bound

1 thread -- 13 seconds
4 threads -- 10 seconds
8 threads -- 6 seconds
12 threads -- 4 seconds