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Python中單線程、多線程和多進程的效率對比實驗

高洛峰

Nov 16, 2016 am 10:10 AM

對比實驗

資料顯示，如果多執行緒的進程是CPU密集型的，那多執行緒並不能有多少效率上的提升，相反還可能會因為執行緒的頻繁切換，導致效率下降，推薦使用多進程；如果是IO密集型，多執行緒程序可以利用IO阻塞等待時的空閒時間執行其他執行緒，提升效率。所以我們根據實驗比較不同場景的效率

| 作業系統| CPU | 記憶體| 硬碟|
|-----------|-------|------| --------|
| Windows 10 | 雙核心|8GB|機械硬碟|

(1)引進所需的模組

import requests
import time
from threading import Thread
from multiprocessing import Process

(2)定義CPU密集的運算函數

def count(x, y):
    # 使程序完成150万计算
    c = 0
    while c < 500000:
        c += 1
        x += x
        y += y

（3）定義IO密集的文件讀寫函數

def write():
    f = open("test.txt", "w")
    for x in range(5000000):
        f.write("testwrite\n")
    f.close()

def read():
    f = open("test.txt", "r")
    lines = f.readlines()
    f.close()

(4) 定義網路請求函數

_head = {
            &#39;User-Agent&#39;: &#39;Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.116 Safari/537.36&#39;}
url = "http://www.tieba.com"
def http_request():
    try:
        webPage = requests.get(url, headers=_head)
        html = webPage.text
        return {"context": html}
    except Exception as e:
        return {"error": e}

(5)測試線性執行IO密集操作、CPU密集操作所需時間、網路請求密集型操作所需時間

# CPU密集操作
t = time.time()
for x in range(10):
    count(1, 1)
print("Line cpu", time.time() - t)

# IO密集操作
t = time.time()
for x in range(10):
    write()
    read()
print("Line IO", time.time() - t)

# 网络请求密集型操作
t = time.time()
for x in range(10):
    http_request()
print("Line Http Request", time.time() - t)

輸出

CPU密集：95.6059999466、91.57099986076355 92.52800011634827、IO 99.96799993515015

密0926514、22.060999870300293

網路請求密集型: 14697

(6)測試多執行緒並發執行CPU密集操作所需時間

counts = []
t = time.time()
for x in range(10):
    thread = Thread(target=count, args=(1,1))
    counts.append(thread)
    thread.start()

e = counts.__len__()
while True:
    for th in counts:
        if not th.is_alive():
            e -= 1
    if e <= 0:
        break
print(time.time() - t)

Output: 99.9240000248 、101.26400017738342、102.32200002670288rr

(7)測試多加執行緒並發執行操作密集時間88、24.02400016784668

(8)測試多執行緒並發執行網路密集操作所需時間

def io():
    write()
    read()

t = time.time()
ios = []
t = time.time()
for x in range(10):
    thread = Thread(target=count, args=(1,1))
    ios.append(thread)
    thread.start()

e = ios.__len__()
while True:
    for th in ios:
        if not th.is_alive():
            e -= 1
    if e <= 0:
        break
print(time.time() - t)

Output: 0.7419998645782471、0.3839998245239258、0.3900001049041748

(9393001049041748

非常07629395、53.437999963760376

(10)測試多進程並發執行IO密集型操作

t = time.time()
ios = []
t = time.time()
for x in range(10):
    thread = Thread(target=http_request)
    ios.append(thread)
    thread.start()

e = ios.__len__()
while True:
    for th in ios:
        if not th.is_alive():
            e -= 1
    if e <= 0:
        break
print("Thread Http Request", time.time() - t)

Output: 12.509000062942505、13.059000015258789

(11)測試多進程並發執行Http請求密集型操作

counts = []
t = time.time()
for x in range(10):
    process = Process(target=count, args=(1,1))
    counts.append(process)
    process.start()
e = counts.__len__()
while True:
    for th in counts:
        if not th.is_alive():
            e -= 1
    if e <= 0:
        break
print("Multiprocess cpu", time.time() - t)

(11)測試多進程並發執行Http請求密集型操作

t = time.time()
ios = []
t = time.time()
for x in range(10):
    process = Process(target=io)
    ios.append(process)
    process.start()

e = ios.__len__()
while True:
    for th in ios:
        if not th.is_alive():
            e -= 1
    if e <= 0:
        break
print("Multiprocess IO", time.time() - t)

5718994

實驗結果

Python中單線程、多線程和多進程的效率對比實驗透過上面的結果，我們可以看到：