Python进程池multiprocessing.Pool

news/2024/7/27 7:39:13/文章来源:https://blog.csdn.net/weixin_50005386/article/details/135628806

环境：

鲲鹏920:192核心
内存：756G
python：3.9

python单进程的耗时

在做单纯的cpu计算的场景，使用单进程核多进程的耗时做如下测试：
单进程情况下cpu的占用了如下，占用一半的核心数：
在这里插入图片描述

每一步和总耗时如下：
单进程耗时

多进程

cpu占用如下，每个进程基本占用48个左右核心数；
在这里插入图片描述
多进程的耗时如下：
每一个进程的耗时为63s左右，总的耗时比单进程还多，如果绑定48核心到每个进程，耗时更高。这是为何？

是否可以得出结论，在cpu计算密集的场景，单进程（每个任务都是独立的、排除IO、竞争关系）的效率会比多进程会高呢?
注：同样的代码在x86服务器上测试过，结论依旧是单进程耗时比多进程会少，这是为什么？

样例代码

from sklearn.datasets import load_wine
from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler, RobustScaler
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
import time
from multiprocessing import Process, Pool, current_process
import multiprocessing
import numpy as np
import os
import psutilimport oscore_count = os.cpu_count()
print(f"The CPU has {core_count} cores.")cpu_cores = [index for index in range(0, core_count)]def task1(data):start = time.time()X = np.random.rand(178, 13)y = np.random.randint(low=0, high=3, size=(178))X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=60)mm_scaler = MinMaxScaler()X_train = mm_scaler.fit_transform(X_train)X_test = mm_scaler.fit_transform(X_test)mlp = MLPClassifier(solver='lbfgs', hidden_layer_sizes=[500, 500], max_iter=300, random_state=60)mlp.fit(X_train, y_train)# print("***" * 10, "current data value:{}".format(data))# print("******************************************current processid:{} end id is {}".format(multiprocessing.current_process().name, data))print("this step spend time is {} seconds".format(time.time() - start))# time.sleep(5)def task(data):process = current_process()print(process)pid = os.getpid()index = process._identity[0]cores = cpu_cores[(index-1) * 48 : index * 48]# print("process:{}, pid:{}, index:{}, core:{}".format(process, pid, index, cores))p = psutil.Process(pid)  # 通过进程 ID 获取进程对象# p.cpu_affinity(cores)  # 绑定核心start = time.time()X = np.random.rand(178, 13)y = np.random.randint(low=0, high=3, size=(178))X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=60)mm_scaler = MinMaxScaler()X_train = mm_scaler.fit_transform(X_train)X_test = mm_scaler.fit_transform(X_test)mlp = MLPClassifier(solver='lbfgs', hidden_layer_sizes=[500, 500], max_iter=300, random_state=60)mlp.fit(X_train, y_train)print("this step spend time is {} seconds".format(time.time() - start))def main():data = [i for i in range(4)]start = time.time()for item in data:task1(item)print("single spend time is ", time.time() - start, " seconds")start = time.time()with Pool(4) as pool:pool.map_async(task, data)pool.close()pool.join()print("spend time is ", time.time() - start, " seconds")if __name__ == '__main__':main()