在python中,multiprocess.Queue.get()需要很长时间

我需要通过遍历数据来获取真正大的数据块。我总共需要几百万次迭代。所以我以为减价会加快我的处理速度,结果就差不多了。我用 subprocess.Queue 要调用不同的线程,这实际上很好,但是当我调用*subprocess.Queue.get()时,程序将永远获取结果也许我做错了什么。下面是我的一个小例子:

def get_losses(self, tags=None):
    return_dict = {}
    output_list = multiprocessing.Queue()
    process_list = []

    # Create quese definition
    for experiment, path in self.tf_board_dicts.items():
        t = multiprocessing.Process(target=self._load_vec_from_tfboard, args=(path, tags, experiment))
        process_list.append(t)
    print("Starting subprocesse with a total of {} workers. \n These are  {}".format(len(process_list),
                                                                                         process_list))
    # Run processes
    for p in process_list:
        p.start()

    # Exit the finished threads
    for p in process_list:
        p.join()
    print("All subprocesses are termianted")

    # Get results
    results = [output_list.get() for p in process_list]
    print("All losses are gathered: {}".format([tup[0] for tup in results]))

    # Create dict
    for experiment_losses in results:
         return_dict[experiment_losses[0]] = experiment_losses[1]

    return return_dict