[SciPy-dev] Implementation of a parallel cKDTree
Fri Feb 27 10:27:36 CST 2009
I have fiddled a bit with scipy.spatial.cKDTree for better performance
on multicore CPUs. I have used threading.Thread instead of OpenMP, so no
special compilation or compiler is required. The number of threads
defaults to the number of processors if it can be determined. The
performance is not much different from what I get with OpenMP. It is
faster than using cKDTree with multiprocessing and shared memory.
Memory handling is also improved. There are checks for NULL pointers
returned by malloc or realloc. setjmp/longjmp is used for error handling
if malloc or realloc fail. A memory pool is used to make sure all
complex data structures are cleaned up properly.
I have assumed that crt functions malloc, realloc and free are thread
safe. This is usually the case. If they are not, they must be wrapped
with calls to PyGILState_Ensure and PyGILState_Release. I have not done
this as it could impair scalability.
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