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:
每一步和总耗时如下:
多进程
cpu占用如下,每个进程基本占用48个左右核心数;
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?文章来源:https://www.toymoban.com/news/detail-797914.html
样例代码文章来源地址https://www.toymoban.com/news/detail-797914.html
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 psutil
import os
core_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()
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