multiprocessing.Pool示例

Question

我正在尝试学习如何使用多处理,并找到以下示例.

我想总结如下值:

from multiprocessing import Pool
from time import time

N = 10
K = 50
w = 0

def CostlyFunction(z):
    r = 0
    for k in xrange(1, K+2):
        r += z ** (1 / k**1.5)
    print r
    w += r
    return r

currtime = time()

po = Pool()

for i in xrange(N):
    po.apply_async(CostlyFunction,(i,))
po.close()
po.join()

print w
print '2: parallel: time elapsed:', time() - currtime

我无法得到所有r值的总和.

Answer 1

如果你打算像那样使用apply_async,那么你必须使用某种共享内存.此外,您需要放置启动多处理的部分,以便仅在初始脚本调用时完成,而不是通过池化进程调用.这是用map做的一种方法.

from multiprocessing import Pool
from time import time

K = 50
def CostlyFunction((z,)):
    r = 0
    for k in xrange(1, K+2):
        r += z ** (1 / k**1.5)
    return r

if __name__ == "__main__":
    currtime = time()
    N = 10
    po = Pool()
    res = po.map_async(CostlyFunction,((i,) for i in xrange(N)))
    w = sum(res.get())
    print w
    print '2: parallel: time elapsed:', time() - currtime

Answer 2

这是我在python示例文档中找到的最简单的示例:

from multiprocessing import Pool

def  f(x):
    return x*x

if __name__ == '__main__':
    pool = Pool(processes=4)              # start 4 worker processes
    result = pool.apply_async(f, [10])    # evaluate "f(10)" asynchronously
    print result.get(timeout=1)           # prints "100" unless your computer is *very* slow
    print pool.map(f, range(10))          # prints "[0, 1, 4,..., 81]"

即使我能理解它也很简单.
注意result.get()是触发计算的原因.