Optimizing Many Matrix Operations in Python / Numpy
In writing some numerical analysis code, I have bottle-necked at a function that requires many Numpy calls. I am not entirely sure how to approach further performance optimization.
Problem:
The function determines error by calculating the following,
Code:
def foo(B_Mat, A_Mat):
Temp = np.absolute(B_Mat)
Temp /= np.amax(Temp)
return np.sqrt(np.sum(np.absolute(A_Mat - Temp*Temp))) / B_Mat.shape[0]
What would be the best way to squeeze some extra performance out of the code? Would my best course of action be performing the majority of the operations in a single for loop with Cython to cut down on the temporary arrays?
There are specific functions from the implementation that could be off-loaded to numexpr module which is known to be very efficient for arithmetic computations. For our case, specifically we could perform squaring, summation and absolute computations with it. Thus, a numexpr based solution to replace the last step in the original code, would be like so -
import numexpr as ne
out = np.sqrt(ne.evaluate('sum(abs(A_Mat - Temp**2))'))/B_Mat.shape[0]
A further performance boost could be achieved by embedding the normalization step into the numexpr 's evaluate expression. Thus, the entire function modified to use numexpr would be -
def numexpr_app1(B_Mat, A_Mat):
Temp = np.absolute(B_Mat)
M = np.amax(Temp)
return np.sqrt(ne.evaluate('sum(abs(A_Mat*M**2-Temp**2))'))/(M*B_Mat.shape[0])
Runtime test -
In [198]: # Random arrays
...: A_Mat = np.random.randn(4000,5000)
...: B_Mat = np.random.randn(4000,5000)
...:
In [199]: np.allclose(foo(B_Mat, A_Mat),numexpr_app1(B_Mat, A_Mat))
Out[199]: True
In [200]: %timeit foo(B_Mat, A_Mat)
1 loops, best of 3: 891 ms per loop
In [201]: %timeit numexpr_app1(B_Mat, A_Mat)
1 loops, best of 3: 400 ms per loop
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