python中的多cpu并行编程

pip install pp

#-*- coding: UTF-8 -*-
import math, sys, time
import pp
def IsPrime(n):
    """返回n是否是素数"""
    if not isinstance(n, int):
        raise TypeError("argument passed to is_prime is not of 'int' type")
    if n < 2:
        return False
    if n == 2:
        return True
    max = int(math.ceil(math.sqrt(n)))
    i = 2
    while i <= max:
        if n % i == 0:
            return False
        i += 1
    return True
def SumPrimes(n):
    for i in xrange(15):
        sum([x for x in xrange(2,n) if IsPrime(x)])
    """计算从2-n之间的所有素数之和"""
    return sum([x for x in xrange(2,n) if IsPrime(x)])
inputs = (100000, 100100, 100200, 100300, 100400, 100500, 100600, 100700)
# start_time = time.time()
# for input in inputs:
#     print SumPrimes(input)
# print '单线程执行，总耗时', time.time() - start_time, 's'
# # tuple of all parallel python servers to connect with
ppservers = ()
#ppservers = ("10.0.0.1",)
if len(sys.argv) > 1:
    ncpus = int(sys.argv[1])
    # Creates jobserver with ncpus workers
    job_server = pp.Server(ncpus, ppservers=ppservers)
else:
    # Creates jobserver with automatically detected number of workers
    job_server = pp.Server(ppservers=ppservers)
print "pp 可以用的工作核心线程数", job_server.get_ncpus(), "workers"
start_time = time.time()
jobs = [(input, job_server.submit(SumPrimes,(input,), (IsPrime,), ("math",))) for input in inputs]#submit提交任务
for input, job in jobs:
    print "Sum of primes below", input, "is", job()# job()获取方法执行结果
print "多线程下执行耗时: ", time.time() - start_time, "s"
job_server.print_stats()#输出执行信息

def submit(self, func, args=(), depfuncs=(), modules=(),
        callback=None, callbackargs=(), group='default', globals=None):
    """Submits function to the execution queue
 
        func - function to be executed  执行的方法
        args - tuple with arguments of the 'func' 方法传入的参数，使用元组
        depfuncs - tuple with functions which might be called from 'func' 传入自己写的方法 ，元组
        modules - tuple with module names to import  传入 模块
        callback - callback function which will be called with argument
                list equal to callbackargs+(result,)
                as soon as calculation is done
        callbackargs - additional arguments for callback function
        group - job group, is used when wait(group) is called to wait for
        jobs in a given group to finish
        globals - dictionary from which all modules, functions and classes
        will be imported, for instance: globals=globals()
    """

# 多进程
import multiprocessing as mp
def job(q, a, b):
    print('aaa')
    q.put(a**1000+b*1000)  # 把计算结果放到队列
# 多进程
if __name__ == '__main__':
    q = mp.Queue()  # 一个队列
    p1 = mp.Process(target=job, args=(q, 100, 200))
    p2 = mp.Process(target=job, args=(q, 100, 200))
    p1.start()
    p1.join()
    # print(p1.ident)
    p2.start()
    p2.join()
    res = q.get() + q.get()  # 读取队列，这里面保存了两次计算得到的结果
    print('result:', res)