在Windows上的Python中演示多核加速的一些示例代码是什么？

在Windows上的Python中演示多核加速的一些示例代码是什么？

演示多处理速度非常简单：

import multiprocessingimport sysimport time# multi-platform precision clockget_timer = time.clock if sys.platform == "win32" else time.timedef cube_function(num):    time.sleep(0.01)  # let's simulate it takes ~10ms for the CPU core to cube the number    return num**3if __name__ == "__main__":  # multiprocessing guard    # we'll test multiprocessing with pools from one to the number of CPU cores on the system    # it won't show significant improvements after that and it will soon start going    # downhill due to the underlying OS thread context switches    for workers in range(1, multiprocessing.cpu_count() + 1):        pool = multiprocessing.Pool(processes=workers)        # lets 'warm up' our pool so it doesn't affect our measurements        pool.map(cube_function, range(multiprocessing.cpu_count()))        # now to the business, we'll have 10000 numbers to quart via our expensive function        print("Cubing 10000 numbers over {} processes:".format(workers))        timer = get_timer()  # time measuring starts now        results = pool.map(cube_function, range(10000))  # map our range to the cube_function        timer = get_timer() - timer  # get our delta time as soon as it finishes        print("tTotal: {:.2f} seconds".format(timer))        print("tAvg. per process: {:.2f} seconds".format(timer / workers))        pool.close()  # lets clear out our pool for the next run        time.sleep(1)  # waiting for a second to make sure everything is cleaned up

当然，在这里我们只是模拟10ms /数字的计算，您可以

cube_function

用任何CPU负担的方法代替实际演示。结果符合预期：

Cubing 10000 numbers over 1 processes:        Total: 100.01 seconds        Avg. per process: 100.01 secondsCubing 10000 numbers over 2 processes:        Total: 50.02 seconds        Avg. per process: 25.01 secondsCubing 10000 numbers over 3 processes:        Total: 33.36 seconds        Avg. per process: 11.12 secondsCubing 10000 numbers over 4 processes:        Total: 25.00 seconds        Avg. per process: 6.25 secondsCubing 10000 numbers over 5 processes:        Total: 20.00 seconds        Avg. per process: 4.00 secondsCubing 10000 numbers over 6 processes:        Total: 16.68 seconds        Avg. per process: 2.78 secondsCubing 10000 numbers over 7 processes:        Total: 14.32 seconds        Avg. per process: 2.05 secondsCubing 10000 numbers over 8 processes:        Total: 12.52 seconds        Avg. per process: 1.57 seconds

现在，为什么不100％线性？嗯，首先，它需要一些时间来图/数据分配给子流程，并把它找回来，有一些成本的上下文切换，还有一些用我的CPU不时其他任务，

time.sleep()

不是完全精确（也不可能在非RT
OS上使用）…但是结果大致上可用于并行处理。