Python IO密集型任务、计算密集型任务,以及多线程、多进程

对于IO密集型任务:

  • 直接执行用时:10.0333秒
  • 多线程执行用时:4.0156秒
  • 多进程执行用时:5.0182秒

说明多线程适合IO密集型任务。

 

对于计算密集型任务

  • 直接执行用时:10.0273秒
  • 多线程执行用时:13.247秒
  • 多进程执行用时:6.8377秒

说明多进程适合计算密集型任务。

#coding=utf-8
import sys
import multiprocessing
import time
import threading


# 定义全局变量Queue
g_queue = multiprocessing.Queue()

def init_queue():
    print("init g_queue start")
    while not g_queue.empty():
        g_queue.get()
    for _index in range(10):
        g_queue.put(_index)
    print("init g_queue end")
    return

# 定义一个IO密集型任务:利用time.sleep()
def task_io(task_id):
    print("IOTask[%s] start" % task_id)
    while not g_queue.empty():
        time.sleep(1)
        try:
            data = g_queue.get(block=True, timeout=1)
            print("IOTask[%s] get data: %s" % (task_id, data))
        except Exception as excep:
            print("IOTask[%s] error: %s" % (task_id, str(excep)))
    print("IOTask[%s] end" % task_id)
    return

g_search_list = list(range(10000))
# 定义一个计算密集型任务:利用一些复杂加减乘除、列表查找等
def task_cpu(task_id):
    print("CPUTask[%s] start" % task_id)
    while not g_queue.empty():
        count = 0
        for i in range(10000):
            count += pow(3*2, 3*2) if i in g_search_list else 0
        try:
            data = g_queue.get(block=True, timeout=1)
            print("CPUTask[%s] get data: %s" % (task_id, data))
        except Exception as excep:
            print("CPUTask[%s] error: %s" % (task_id, str(excep)))
    print("CPUTask[%s] end" % task_id)
    return task_id

if __name__ == '__main__':
    print("cpu count:", multiprocessing.cpu_count(), "\n")

    print(u"========== 直接执行IO密集型任务 ==========")
    init_queue()
    time_0 = time.time()
    task_io(0)
    print(u"结束:", time.time() - time_0, "\n")

    print("========== 多线程执行IO密集型任务 ==========")
    init_queue()
    time_0 = time.time()
    thread_list = [threading.Thread(target=task_io, args=(i,)) for i in range(10)]
    for t in thread_list:
        t.start()
    for t in thread_list:
        if t.is_alive():
            t.join()
    print("结束:", time.time() - time_0, "\n")

    print("========== 多进程执行IO密集型任务 ==========")
    init_queue()
    time_0 = time.time()
    process_list = [multiprocessing.Process(target=task_io, args=(i,)) for i in range(multiprocessing.cpu_count())]
    for p in process_list:
        p.start()
    for p in process_list:
        if p.is_alive():
            p.join()
    print("结束:", time.time() - time_0, "\n")

    print("========== 直接执行CPU密集型任务 ==========")
    init_queue()
    time_0 = time.time()
    task_cpu(0)
    print("结束:", time.time() - time_0, "\n")

    print("========== 多线程执行CPU密集型任务 ==========")
    init_queue()
    time_0 = time.time()
    thread_list = [threading.Thread(target=task_cpu, args=(i,)) for i in range(10)]
    for t in thread_list:
        t.start()
    for t in thread_list:
        if t.is_alive():
            t.join()
    print("结束:", time.time() - time_0, "\n")

    print("========== 多进程执行cpu密集型任务 ==========")
    init_queue()
    time_0 = time.time()
    process_list = [multiprocessing.Process(target=task_cpu, args=(i,)) for i in range(multiprocessing.cpu_count())]
    for p in process_list:
        p.start()
    for p in process_list:
        if p.is_alive():
            p.join()
    print("结束:", time.time() - time_0, "\n")

参考:https://zhuanlan.zhihu.com/p/24283040

posted on 2018-08-20 11:11  tsw123  阅读(2701)  评论(0编辑  收藏  举报

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