I\'ve been having trouble with speed while trying to utilise the gmpy module.
import numpy as np
import gmpy2 as gm
N = 1000
a = range(N)
%timeit [gm.sin(x) for
I was curious to see how much performance increase would be possible so wrote a new function for gmpy2
that calculated the sin
of a list entirely in C. Unfortunately, there wasn't much improvement.
%timeit [gmpy2.sin(x) for x in a]
100 loops, best of 3: 4.85 ms per loop
%timeit map(gmpy2.sin, a)
100 loops, best of 3: 4.59 ms per loop
%timeit gmpy2.vector(a)
100 loops, best of 3: 4.44 ms per loop
gmpy2
does not release the Global Interpreter Lock (GIL) so threading won't help.
Multiprocessing may help but you will probably need to parallelize portions of code that take seconds (or longer) to execute to overcome the overhead of passing data to another process.
Software-based, arbitrary-precision floating point is just slower than native floating point.