A library for Matrix Multiplication
Author: Martin Smith
Created on: July 18, 2012
Last updated: July 20, 2012

Usage:

    - As a library: Mullib.py has a matrix_multiply(A,B) function that will
      expose the fastest matrix multiplication method available to the library
      (as determined at runtime).

    - As an experimental system: Mullib.py has a matrix_multiply_test(A,B)
      function that will allow you to test the speeds of all available
      methods of matrix multiply.

Contents:

    - naiveFunctions.py: Various functions for performing matrix multiply:
        - naiveMul: Naive python nested loop version of matrix multiply
        - numpyMul: Numpy's .dot() function wich farms the operation out to
          BLAS (which is in C)
    - oclFunctions.py: Various kernels in a PyOpenCL framework for GPU and
      CPU accelleration.
        - MatMul: unoptimized but CPU and GPU compatible version of matrix
          multiply. Very simple, very robust.


More coming!