Python中的多处理比单线程慢

Question

我最初的问题是关于Python下的并行性。然而，由于这个问题一直没有答案，我删除了它，并试图总结我的结论。希望它能帮助某人。。。在

一般来说，有两种主要的方法使代码并行运行-使用多线程或多处理库。在

根据上的许多帖子stackoverflow.com网站多线程库能够跨线程有效地共享内存，但在单个内核上运行线程。因此，如果瓶颈是I/O操作，它可以提高代码的速度。我不确定是否有许多图书馆的实际应用程序。。。在

你的CPU问题有时会被称为多处理器问题。库将线程分散到各个核心上。然而，许多人（包括我）观察到，这样的多核代码比单核代码要慢得多。这个问题被认为是由于单个线程不能有效地共享内存-数据被大量复制，这会造成相当大的开销。正如我下面的代码所示，开销很大程度上取决于输入数据类型。这个问题在Windows上比在Linux上要严重得多。我不得不说，并行性是我对Python最大的失望——显然Python的设计并没有考虑到并行性。。。在

第一段代码使用Process在核心之间分配{}。在

import numpy as np
import math as mth
import pandas as pd
import time as tm
import multiprocessing as mp

def bnd_calc_npv_dummy(bnds_info, core_idx, npv):
    """ multiple core dummy valuation function (based on single core function) """

    bnds_no = len(bnds_info)
    tm.sleep(0.0001 * bnds_no)

    npv[core_idx] = np.array(bnds_info['npv'])

def split_bnds_info(bnds_info, cores_no):
    """ cut dataframe with bond definitions into pieces - one piece per core """

    bnds_info_mp = []
    bnds_no = len(bnds_info)
    batch_size = mth.ceil(np.float64(bnds_no) / cores_no) # number of bonds allocated to one core

    # split dataframe among cores
    for idx in range(cores_no):
        lower_bound = int(idx * batch_size)
        upper_bound = int(np.min([(idx + 1) * batch_size, bnds_no]))
        bnds_info_mp.append(bnds_info[lower_bound : upper_bound].reset_index().copy())

    # return list of dataframes
    return bnds_info_mp

def bnd_calc_npv(bnds_info, cores_no):
    """ dummy valuation function running multicore """

    manager = mp.Manager()
    npv = manager.dict()

    bnds_info_mp = split_bnds_info(bnds_info, cores_no)

    processes = [mp.Process(target = bnd_calc_npv_dummy, args = (bnds_info_mp[core_idx], core_idx, npv)) for core_idx in xrange(cores_no)]     
    [process.start() for process in processes]     
    [process.join() for process in processes]

    # return NPV of individual bonds    
    return np.hstack(npv.values())

if __name__ == '__main__':

    # create dummy dataframe
    bnds_no = 1200 # number of dummy in the sample
    bnds_info = {'currency_name' : 'EUR', 'npv' : 100}
    bnds_info = pd.DataFrame(bnds_info, index = range(1))
    bnds_info = pd.concat([bnds_info] * bnds_no, ignore_index = True)

    # one core
    print("ONE CORE")
    start_time = tm.time()
    bnds_no = len(bnds_info)
    tm.sleep(0.0001 * bnds_no)
    npv = np.array(bnds_info['npv'])
    elapsed_time = (tm.time() - start_time)
    print('   elapsed time: ' + str(elapsed_time) + 's')

    # two cores
    print("TWO CORES")
    cores_no = 2
    start_time = tm.time()
    npv = bnd_calc_npv(bnds_info, cores_no)
    elapsed_time = (tm.time() - start_time)
    print('   elapsed time: ' + str(elapsed_time) + 's')

    # three cores
    print("THREE CORES")
    cores_no = 3
    start_time = tm.time()
    npv = bnd_calc_npv(bnds_info, cores_no)
    elapsed_time = (tm.time() - start_time)
    print('  elapsed time: ' + str(elapsed_time) + 's')

    # four cores
    print("FOUR CORES")
    cores_no = 4
    start_time = tm.time()
    npv = bnd_calc_npv(bnds_info, cores_no)
    elapsed_time = (tm.time() - start_time)
    print('  elapsed time: ' + str(elapsed_time) + 's')

第二个代码与前面的代码相同-唯一的区别是这次我们使用numpy array而不是{}，性能差异很大（比较单核的运行时变化和多核的运行时变化）。在

^{pr2}$

最后一段代码使用Pool，而不是Process。运行时稍微好一点。在

import numpy as np
import time as tm
import multiprocessing as mp

#import pdb
#pdb.set_trace()

def bnd_calc_npv_dummy(bnds_info):
    """ multiple core dummy valuation function (based on single core function) """

    try:
        # get number of bonds
        bnds_no = len(bnds_info)
    except:
        pass
        bnds_no = 1

        tm.sleep(0.0001 * bnds_no)

    return bnds_info

def bnd_calc_npv(bnds_info, cores_no):
    """ dummy valuation function running multicore """

    pool = mp.Pool(processes = cores_no)
    npv = pool.map(bnd_calc_npv_dummy, bnds_info.tolist()) 

    # return NPV of individual bonds    
    return npv

if __name__ == '__main__':

    # create dummy dataframe
    bnds_no = 1200 # number of dummy in the sample
    bnds_info = np.array([100.0] * bnds_no)

    # one core
    print("ONE CORE")
    start_time = tm.time()
    bnds_no = len(bnds_info)
    tm.sleep(0.0001 * bnds_no)
    elapsed_time = (tm.time() - start_time)
    print('   elapsed time: ' + str(elapsed_time) + 's')

    # two cores
    print("TWO CORES")
    cores_no = 2
    start_time = tm.time()
    npv = bnd_calc_npv(bnds_info, cores_no)
    elapsed_time = (tm.time() - start_time)
    print('   elapsed time: ' + str(elapsed_time) + 's')

    # three cores
    print("THREE CORES")
    cores_no = 3
    start_time = tm.time()
    npv = bnd_calc_npv(bnds_info, cores_no)
    elapsed_time = (tm.time() - start_time)
    print('  elapsed time: ' + str(elapsed_time) + 's')

    # four cores
    print("FOUR CORES")
    cores_no = 4
    start_time = tm.time()
    npv = bnd_calc_npv(bnds_info, cores_no)
    elapsed_time = (tm.time() - start_time)
    print('  elapsed time: ' + str(elapsed_time) + 's')

因此，我的结论是Python实现的并行性并不适用于现实生活（我使用了python2.7.13和windows7）。 谨致问候

麦基

PS：如果有人能够更改代码，我将非常高兴地改变我的想法。。。在

Answer 1

当问题的一部分可以独立计算时，多处理的效果最好，例如使用multiprocessing.Pool。 池中的每个工作进程处理部分输入并将结果返回给主进程。在

如果所有进程都需要修改整个输入数组中的数据，那么manager的同步开销很可能会破坏多处理带来的任何收益。在