Benchmark for threaded XND vs. NumPy

bench.py

import sys, time
import gumath as gm
import gumath.functions as fn
from xnd import xnd
import numpy as np
import gc


#
# The array sizes are for >=16GB of memory.  Your system might freeze if
# you have less than that.
#

#
# Set max_threads to another value (default is num_cpus). In some cases,
# setting max_threads > num_cpus yields a small additional speedup:
#
# gm.set_max_threads(64)
#

max_threads = gm.get_max_threads()

print("\n\n========== sin: max_threads=%d =========\n" % max_threads)

a = np.arange(0, 1, 1/200000000.0, dtype="float64")
x = xnd.from_buffer(a)

start = time.time()
z = fn.sin(x)
end = time.time()
print("xnd: ", end-start)

start = time.time()
c = np.sin(a)
end = time.time()
print("np: ", end-start)

assert np.all(z == c)
del a, x, z, c
gc.collect()
print("\n\n========== multiply: max_threads=%d =========\n" % max_threads)

a = np.arange(150000000.0, dtype="float64")
b = np.arange(150000000.0, dtype="float64")
x = xnd.from_buffer(a)
y = xnd.from_buffer(b)

start = time.time()
z = fn.multiply(x, y)
end = time.time()
print("xnd: ", end-start)

start = time.time()
c = np.multiply(a, b)
end = time.time()
print("np: ", end-start)

assert np.all(z == c)
del a, b, x, y, z, c
gc.collect()