Python中的并行编程速度
这里主要想记录下今天碰到的一个小知识点：Python中的并行编程速率如何？
我想把AutoTool做一个并行化改造，主要目的当然是想提高多任务的执行速度。第一反应就是想到用多线程执行不同模块任务，但是在我收集Python多线程编程资料的时候发现一个非常奇怪的信息，那就是Python的多线程并不是真正的多线程，因为有一个GIL的存在（可以参考这篇文章讲解《Python最难的问题》）导致Python实际上默认（CPython解释器）只能是单线程执行。
这里我写了一个例子可以看看：
1 #!/usr/bin/env python2 # -*- coding: utf-8 -*-
3 # @File : batch_swig_runner.py
4 # @Time : 2019/7/8 18:09
5 # @Author : KuLiuheng
6 # @Email : liuheng.klh@alibaba-inc.com
7
8 from swig_runner import SwigRunner
9
10 import time
11 import logging
12 from threading import Thread
13 from multiprocessing import Pool
14
15
16 class TestRunner(Thread):
17 def __init__(self, name, path):
18 super(TestRunner, self).__init__()
19 self.name = name
20 self.path = path
21
22 def run(self):
23 logging.warning("Message from the thread-%s START" % self.name)
24 for i in range(10000000): # 耗时操作模拟
25 j = int(i) * 10.1
26 # time.sleep(1)
27 logging.warning("Message from the thread-%s END" % self.name)
28 return self.path
29
30
31 def multi_process(mname, mpath):
32 logging.warning("Message from the thread-%s START" % mname)
33 for i in range(10000000): # 耗时操作模拟
34 j = int(i) * 10.1
35 # time.sleep(1)
36 logging.warning("Message from the thread-%s END" % mname)
37
38
39 class BatchSwigRunner(object):
40 def __init__(self, modules=None):
41 """
42 用模块信息字典（工程名: 工程路径）来初始化
43 :param modules: {工程名: 工程路径}
44 """
45 if modules is not None:
46 self._modules = modules
47 else:
48 self._modules = dict()
49
50 def add_module_info(self, name, path):
51 self._modules[name] = path
52
53 def start(self):
54 """
55 启动批量任务执行，并返回执行过程中的错误信息
56 :return: list(工程序号，工程名称) 出错的工程信息列表
57 """
58 runners = list()
59 for (project_name, project_path) in self._modules.items():
60 # logging.warning('BatchSwigRunner.start() [%s][%s]' % (project_name, project_path))
61 sub_runner = TestRunner(project_name, project_path)
62 sub_runner.daemon = True
63 sub_runner.start()
64 runners.append(sub_runner)
65
66 for runner in runners:
67 runner.join()
68
69
70 if __name__ == '__main__':
71 batch_runner = BatchSwigRunner()
72 batch_runner.add_module_info('name1', 'path1')
73 batch_runner.add_module_info('name2', 'path2')
74 batch_runner.add_module_info('name3', 'path3')
75 batch_runner.add_module_info('name4', 'path4')
76 start_time = time.time()
77 batch_runner.start()
78
79 print 'Total time comsumed = %.2fs' % (time.time() - start_time)
80
81 print('========================================')
82 start_time = time.time()
83
84 for index in range(4):
85 logging.warning("Message from the times-%d START" % index)
86 for i in range(10000000): # 耗时操作模拟
87 j = int(i) * 10.1
88 # time.sleep(1)
89 logging.warning("Message from the times-%d END" % index)
90
91 print '>>Total time comsumed = %.2fs' % (time.time() - start_time)
92
93 print('----------------------------------------------')
94 start_time = time.time()
95
96 pool = Pool(processes=4)
97 for i in range(4):
98 pool.apply_async(multi_process, ('name++%d' % i, 'path++%d' % i))
99 pool.close()
100 pool.join()
101 print '>>>> Total time comsumed = %.2fs' % (time.time() - start_time)
View Code
看结果就发现很神奇的结论：
C:\Python27\python.exe E:/VirtualShare/gitLab/GBL-310/GBL/AutoJNI/autoTool/common/batch_swig_runner.pyWARNING:root:Message from the thread-name4 START
WARNING:root:Message from the thread-name2 START
WARNING:root:Message from the thread-name3 START
WARNING:root:Message from the thread-name1 START
WARNING:root:Message from the thread-name2 END
WARNING:root:Message from the thread-name4 END
WARNING:root:Message from the thread-name3 END
Total time comsumed = 15.92s
========================================
WARNING:root:Message from the thread-name1 END
WARNING:root:Message from the times-0 START
WARNING:root:Message from the times-0 END
WARNING:root:Message from the times-1 START
WARNING:root:Message from the times-1 END
WARNING:root:Message from the times-2 START
WARNING:root:Message from the times-2 END
WARNING:root:Message from the times-3 START
WARNING:root:Message from the times-3 END
>>Total time comsumed = 11.59s
----------------------------------------------
WARNING:root:Message from the thread-name++0 START
WARNING:root:Message from the thread-name++1 START
WARNING:root:Message from the thread-name++2 START
WARNING:root:Message from the thread-name++3 START
WARNING:root:Message from the thread-name++1 END
WARNING:root:Message from the thread-name++0 END
WARNING:root:Message from the thread-name++2 END
WARNING:root:Message from the thread-name++3 END
>>>> Total time comsumed = 5.69s
Process finished with exit code 0
View Code
其运行速度是（计算密集型）：multiprocessing > normal > threading.Thread
请注意这里用的是持续计算来模拟耗时操作：
for i in range(10000000): # 耗时操作模拟j = int(i) * 10.1
如果用空等待（time.sleep(1)类似IO等待）来模拟耗时操作，那么结果就是（IO等待型）：threading.Thread > multiprocessing > normal
C:\Python27\python.exe E:/VirtualShare/gitLab/GBL-310/GBL/AutoJNI/autoTool/common/batch_swig_runner.pyWARNING:root:Message from the thread-name4 START
WARNING:root:Message from the thread-name2 START
WARNING:root:Message from the thread-name3 START
WARNING:root:Message from the thread-name1 START
WARNING:root:Message from the thread-name3 END
WARNING:root:Message from the thread-name4 END
WARNING:root:Message from the thread-name2 END
WARNING:root:Message from the thread-name1 END
WARNING:root:Message from the times-0 START
Total time comsumed = 1.01s
========================================
WARNING:root:Message from the times-0 END
WARNING:root:Message from the times-1 START
WARNING:root:Message from the times-1 END
WARNING:root:Message from the times-2 START
WARNING:root:Message from the times-2 END
WARNING:root:Message from the times-3 START
WARNING:root:Message from the times-3 END
>>Total time comsumed = 4.00s
----------------------------------------------
WARNING:root:Message from the thread-name++0 START
WARNING:root:Message from the thread-name++1 START
WARNING:root:Message from the thread-name++2 START
WARNING:root:Message from the thread-name++3 START
WARNING:root:Message from the thread-name++0 END
WARNING:root:Message from the thread-name++1 END
WARNING:root:Message from the thread-name++2 END
WARNING:root:Message from the thread-name++3 END
>>>> Total time comsumed = 1.73s
Process finished with exit code 0
View Code
为何会有这样的结果呢？
（1）threading机制中因为GIL的存在，实际上是一把全局锁让多线程变成了CPU线性执行，只可能用到一颗CPU计算。当sleep这样是释放CPU操作发生时，可以迅速切换线程，切换速度可以接受（比multiprocessing快），比normal（阻塞等待）当然快的多；
（2）这里用了多进程Pool，可以真正意义上使用多CPU，对于CPU计算密集型的操作（上面的for循环计算）那么肯定是多核比单核快。所以就出现了第一种测试场景的结果。
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