Python multiprocessing 多进程使用例子，,#! /usr/bin/

Python multiprocessing 多进程使用例子，,#! /usr/bin/

#! /usr/bin/env pythonimport mathimport randomimport Queueimport multiprocessingimport timedef normal_pdf(x, mean, sd):    return ( (1.0 / math.sqrt(2 * math.pi * pow(sd, 2)))            * math.exp(-1 * float(pow(x - mean, 2))/(2 * pow(sd, 2))) )def log_prob(x, mean, sd):    if hasattr(x, "__iter__"):        return sum([log_prob(i, mean, sd) for i in x])    else:        return math.log(normal_pdf(x, mean, sd))class Worker(multiprocessing.Process):    def __init__(self,            work_queue,            result_queue,            mean=0,            sd=1):        # base class initialization        multiprocessing.Process.__init__(self)        # job management stuff        self.work_queue = work_queue        self.result_queue = result_queue        self.kill_received = False        # job execution stuff        self.mean = mean        self.sd = sd    def run(self):        while not self.kill_received:            # get a task            try:                job = self.work_queue.get_nowait()            except Queue.Empty:                break            # the actual processing            log_prob = sum([math.log(normal_pdf(i, self.mean, self.sd))                    for i in job])            # store the result            self.result_queue.put(log_prob)def execute(jobs, num_processes=2):    # load up work queue    work_queue = multiprocessing.Queue()    for job in jobs:        work_queue.put(job)    # create a queue to pass to workers to store the results    result_queue = multiprocessing.Queue()    # spawn workers    for i in range(num_processes):        worker = Worker(work_queue, result_queue, mean=0, sd=1)        worker.start()    # collect the results off the queue    results = []    while len(results)