multiprocess_test.py

import random
import time
import functools
from multiprocessing import Pool

def Timer(func):
	@functools.wraps(func)
	def Wrapper(*args, **kw):
		stime = time.clock()
		ret = func(*args, **kw)
		etime = time.clock()
		print('{0}: {1:,f}ms'.format(func.__name__, (etime-stime)*1000))
		return ret
	return Wrapper

def dice(iter):
    time.sleep(1)
    return random.randint(1, 6)

@Timer
def parallel():
    with Pool() as pool:
        results_set = pool.map(dice, range(100))
        print(sum(results_set) / len(results_set))

@Timer
def serial():
    results_set = []
    for x in range(100):
        results_set.append(dice(x))
    print(sum(results_set) / len(results_set))

if __name__ == '__main__':
    parallel()
    serial()

# 3.27
# parallel: 28,240.931231ms
# 3.49
# serial: 100,044.868033ms
#
# 並列計算版のほうが圧倒的に早い！

# でもsleepをなくしてみると直列のほうが早い
#
# 3.46
# parallel: 153.485311ms
# 3.6
# serial: 0.365031ms
#
# あまりにも簡単な計算の場合、並列計算の準備コストのほうがでかい