conda install numba
conda install cudatoolkit
In [ ]:
#!/usr/bin/env python
from __future__ import print_function, division, absolute_import
import math
import threading
from timeit import repeat
import numpy as np
from numba import jit
nthreads = 4
size = 10**6
def func_np(a, b):
"""
Control function using Numpy.
"""
return np.exp(2.1 * a + 3.2 * b)
@jit('void(double[:], double[:], double[:])', nopython=True, nogil=True)
def inner_func_nb(result, a, b):
"""
Function under test.
"""
for i in range(len(result)):
result[i] = math.exp(2.1 * a[i] + 3.2 * b[i])
def timefunc(correct, s, func, *args, **kwargs):
"""
Benchmark *func* and print out its runtime.
"""
print(s.ljust(20), end=" ")
# Make sure the function is compiled before we start the benchmark
res = func(*args, **kwargs)
if correct is not None:
assert np.allclose(res, correct), (res, correct)
# time it
print('{:>5.0f} ms'.format(min(repeat(lambda: func(*args, **kwargs),
number=5, repeat=2)) * 1000))
return res
def make_singlethread(inner_func):
"""
Run the given function inside a single thread.
"""
def func(*args):
length = len(args[0])
result = np.empty(length, dtype=np.float64)
inner_func(result, *args)
return result
return func
def make_multithread(inner_func, numthreads):
"""
Run the given function inside *numthreads* threads, splitting its
arguments into equal-sized chunks.
"""
def func_mt(*args):
length = len(args[0])
result = np.empty(length, dtype=np.float64)
args = (result,) + args
chunklen = (length + numthreads - 1) // numthreads
# Create argument tuples for each input chunk
chunks = [[arg[i * chunklen:(i + 1) * chunklen] for arg in args]
for i in range(numthreads)]
# Spawn one thread per chunk
threads = [threading.Thread(target=inner_func, args=chunk)
for chunk in chunks]
for thread in threads:
thread.start()
for thread in threads:
thread.join()
return result
return func_mt
func_nb = make_singlethread(inner_func_nb)
func_nb_mt = make_multithread(inner_func_nb, nthreads)
a = np.random.rand(size)
b = np.random.rand(size)
correct = timefunc(None, "numpy (1 thread)", func_np, a, b)
timefunc(correct, "numba (1 thread)", func_nb, a, b)
timefunc(correct, "numba (%d threads)" % nthreads, func_nb_mt, a, b)