多处理python没有并行运行

我一直在尝试使用python中的多处理模块来实现计算成本高昂的任务的并行性 .

我能够执行我的代码，但它并不是并行运行的 . 我一直在阅读多处理的手册页和foruns，以找出它为什么不工作，我还没有想出来 .

我认为问题可能与执行我创建和导入的其他模块时的某种锁定有关 .

这是我的代码：

main.py：

##import my modules
import prepare_data
import filter_part
import wrapper_part
import utils
from myClasses import ML_set
from myClasses import data_instance

n_proc = 5

def main():
    if __name__ == '__main__':
        ##only main process should run this
        data = prepare_data.import_data() ##read data from file  
        data = prepare_data.remove_and_correct_outliers(data)
        data = prepare_data.normalize_data_range(data)
        features = filter_part.filter_features(data)

        start_t = time.time()
        ##parallelism will be used on this part
        best_subset = wrapper_part.wrapper(n_proc, data, features)

        print time.time() - start_t


main()

wrapper_part.py：

##my modules
from myClasses import ML_set
from myClasses import data_instance
import utils

def wrapper(n_proc, data, features):

    p_work_list = utils.divide_features(n_proc-1, features)
    n_train, n_test = utils.divide_data(data)

    workers = []

    for i in range(0,n_proc-1):
        print "sending process:", i
        p = mp.Process(target=worker_classification, args=(i, p_work_list[i], data, features, n_train, n_test))
        workers.append(p)
        p.start()

    for worker in workers:
        print "waiting for join from worker"
        worker.join()


    return


def worker_classification(id, work_list, data, features, n_train, n_test):
    print "Worker ", id, " starting..."
    best_acc = 0
    best_subset = []
    while (work_list != []):
        test_subset = work_list[0]
        del(work_list[0])
        train_set, test_set = utils.cut_dataset(n_train, n_test, data, test_subset)
        _, acc = classification_decision_tree(train_set, test_set)
        if acc > best_acc:
            best_acc = acc
            best_subset = test_subset
    print id, " found best subset ->  ", best_subset, " with accuracy: ", best_acc

所有其他模块都不使用多处理模块并且工作正常 . 在这个阶段，我只是测试并行测试，甚至没有尝试获得结果，因此进程和共享内存变量之间没有任何通信 . 每个进程都使用一些变量，但它们是在生成进程之前定义的，所以据我所知，我相信每个进程都有自己的变量副本 .

作为5个进程的输出，我得到了这个：

importing data from file...
sending process: 0
sending process: 1
Worker  0  starting...
0  found best subset ->   [2313]  with accuracy:  60.41
sending process: 2
Worker  1  starting...
1  found best subset ->   [3055]  with accuracy:  60.75
sending process: 3
Worker  2  starting...
2  found best subset ->   [3977]  with accuracy:  62.8
waiting for join from worker
waiting for join from worker
waiting for join from worker
waiting for join from worker
Worker  3  starting...
3  found best subset ->   [5770]  with accuracy:  60.07
55.4430000782

4个进程执行并行部分大约需要55秒 . 仅使用1个进程进行测试，执行时间为16秒：

importing data from file...
sending process: 0
waiting for join from worker
Worker  0  starting...
0  found best subset ->   [5870]  with accuracy:  63.32
16.4409999847

我在python 2.7和Windows 8上运行它

EDIT

我在ubuntu上测试了我的代码并且它工作正常，我猜它与Windows 8和python有关 . 这是ubuntu上的输出：

importing data from file...
size trainset:  792  size testset:  302
sending process: 0
sending process: 1
Worker  0  starting...
sending process: 2
Worker  1  starting...
sending process: 3
Worker  2  starting...
waiting for join from worker
Worker  3  starting...
2  found best subset ->   [5199]  with accuracy:  60.93
1  found best subset ->   [3198]  with accuracy:  60.93
0  found best subset ->   [1657]  with accuracy:  61.26
waiting for join from worker
waiting for join from worker
waiting for join from worker
3  found best subset ->   [5985]  with accuracy:  62.25
6.1428809166

我将从现在开始使用ubuntu进行测试，但是我想知道为什么代码在Windows上不起作用 .