为什么增加工人数量(超过核心数量)仍然会减少执行时间？

Question

我始终确信没有必要拥有比CPU内核更多的线程/进程(从性能角度来看).但是,我的python示例显示了不同的结果.

import concurrent.futures
import random
import time


def doSomething(task_num):
    print("executing...", task_num)
    time.sleep(1)  # simulate heavy operation that takes ~ 1 second    
    return random.randint(1, 10) * random.randint(1, 500)  # real operation, used random to avoid caches and so on...


def main():
    # This part is not taken in consideration because I don't want to
    # measure the worker creation time
    executor = concurrent.futures.ProcessPoolExecutor(max_workers=60)

    start_time = time.time()

    for i in range(1, 100): # execute 100 tasks
        executor.map(doSomething, [i, ])
    executor.shutdown(wait=True)

    print("--- %s seconds ---" % (time.time() - start_time))


if __name__ == '__main__':
    main()

计划结果:

1工人--- 100.28233647346497秒---
2工人--- 50.26122164726257秒---
3工人--- 33.32741022109985秒---
4工人--- 25.399883031845093秒---
5工人--- 20.434186220169067秒---
10名工人--- 10.903695344924927秒---
50名工人--- 6.363946914672852秒---
60名工人--- 4.819359302520752秒---

如果只有4个逻辑处理器,这怎么能更快？

这是我的电脑规格(在Windows 8和Ubuntu 14上测试过):

CPU Intel(R)Core(TM)i5-3210M CPU @ 2.50GHz插槽:1个核心:2个 逻辑处理器:4个

Answer 1

原因是因为sleep()只使用了可忽略不计的CPU量.在这种情况下,它是对线程执行的实际工作的不良模拟.

所有sleep()真正的做法是暂停线程,直到计时器到期.线程暂停时,它不使用任何CPU周期.