In this answer to Multiprocessing.Pool makes Numpy matrix multiplication slower the author of the answer recommends in the second paragraph to recompile Numpy with enabled OpenMP directives.
So my questions are:
Searching SO I found following post OpenMP and Python, where the answers explain why there is no use for OpenMP in general Python due to the GIL. But I assume Numpy is a different issue.
While Python code itself hardly benefits from running in parallel, NumPy is not written in Python. It is in fact a pythonistic wrapper around some very well established numerical computational libraries and other numerical algorithms, both implemented in compiled languages like Fortran and C. Some of these libraries already come in parallel multithreaded versions (like Intel MKL and ATLAS, when used to provide BLAS and LAPACK implementations in NumPy).
The idea is that in NumPy programs the Python code should only be used to drive the computations, while all the heavy lifting should be done in the C or Fortran backends. If your program doesn't spend most of its run time inside NumPy routines, then Amdahl's law will prevent you from getting a reasonable speed-up with parallel NumPy.
In order to get NumPy to support OpenMP, you must have an OpenMP-enabled C compiler. Most C compilers nowadays support OpenMP and this includes GCC, Intel C Compiler, Oracle C Compiler, and even Microsoft Visual C Compiler (although it is stuck at an ancient OpenMP version). Read the Installation Manual for detailed instructions.
