Python中单线程、多线程和多进程的效率对比实验

HenryAlarco 4年前
   <p>Python是运行在解释器中的语言,查找资料知道,python中有一个全局锁(GIL),在使用多进程(Thread)的情况下,不能发挥多核的优势。而使用多进程(Multiprocess),则可以发挥多核的优势真正地提高效率。</p>    <h2><strong>对比实验</strong></h2>    <p>资料显示,如果多线程的进程是 <strong>CPU密集型</strong> 的,那多线程并不能有多少效率上的提升,相反还可能会因为线程的频繁切换,导致效率下降,推荐使用多进程;如果是 <strong>IO密集型</strong> ,多线程进程可以利用IO阻塞等待时的空闲时间执行其他线程,提升效率。所以我们根据实验对比不同场景的效率</p>    <table>     <thead>      <tr>       <th>操作系统</th>       <th>CPU</th>       <th>内存</th>       <th>硬盘</th>      </tr>     </thead>     <tbody>      <tr>       <td>Windows 10</td>       <td>双核</td>       <td>8GB</td>       <td>机械硬盘</td>      </tr>     </tbody>    </table>    <p>(1)引入所需要的模块</p>    <pre>  <code class="language-python">importrequests  importtime  fromthreadingimportThread  frommultiprocessingimportProcess  </code></pre>    <p>(2)定义CPU密集的计算函数</p>    <pre>  <code class="language-python">def count(x, y):      # 使程序完成150万计算      c = 0      while c < 500000:          c += 1          x += x          y += y  </code></pre>    <p>(3)定义IO密集的文件读写函数</p>    <pre>  <code class="language-python">defwrite():      f = open("test.txt", "w")      for x in range(5000000):          f.write("testwrite\n")      f.close()     defread():      f = open("test.txt", "r")      lines = f.readlines()      f.close()  </code></pre>    <p>(4) 定义网络请求函数</p>    <pre>  <code class="language-python">_head = {              'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.116 Safari/537.36'}  url = "http://www.tieba.com"  defhttp_request():      try:          webPage = requests.get(url, headers=_head)          html = webPage.text          return {"context": html}      exceptExceptionas e:          return {"error": e}  </code></pre>    <p>(5)测试线性执行IO密集操作、CPU密集操作所需时间、网络请求密集型操作所需时间</p>    <pre>  <code class="language-python"># 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)  </code></pre>    <p>输出</p>    <ul>     <li>CPU密集:95.6059999466、91.57099986076355 92.52800011634827、 99.96799993515015</li>     <li>IO密集:24.25、21.76699995994568、21.769999980926514、22.060999870300293</li>     <li>网络请求密集型: 4.519999980926514、8.563999891281128、4.371000051498413、4.522000074386597、14.671000003814697</li>    </ul>    <p>(6)测试多线程并发执行CPU密集操作所需时间</p>    <pre>  <code class="language-python">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 thin counts:          if not th.is_alive():              e -= 1      if e <= 0:          break  print(time.time() - t)  </code></pre>    <p>Output: 99.9240000248 、101.26400017738342、102.32200002670288</p>    <p>(7)测试多线程并发执行IO密集操作所需时间</p>    <pre>  <code class="language-python">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 thin ios:          if not th.is_alive():              e -= 1      if e <= 0:          break  print(time.time() - t)  </code></pre>    <p>Output: 25.69700002670288、24.02400016784668</p>    <p>(8)测试多线程并发执行网络密集操作所需时间</p>    <pre>  <code class="language-python">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 thin ios:          if not th.is_alive():              e -= 1      if e <= 0:          break  print("Thread Http Request", time.time() - t)  </code></pre>    <p>Output: 0.7419998645782471、0.3839998245239258、0.3900001049041748</p>    <p>(9)测试多进程并发执行CPU密集操作所需时间</p>    <pre>  <code class="language-python">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 thin counts:          if not th.is_alive():              e -= 1      if e <= 0:          break  print("Multiprocess cpu", time.time() - t)  </code></pre>    <p>Output: 54.342000007629395、53.437999963760376</p>    <p>(10)测试多进程并发执行IO密集型操作</p>    <pre>  <code class="language-python">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 thin ios:          if not th.is_alive():              e -= 1      if e <= 0:          break  print("Multiprocess IO", time.time() - t)  </code></pre>    <p>Output: 12.509000062942505、13.059000015258789</p>    <p>(11)测试多进程并发执行Http请求密集型操作</p>    <pre>  <code class="language-python">t = time.time()  httprs = []  t = time.time()  for x in range(10):      process = Process(target=http_request)      ios.append(process)      process.start()     e = httprs.__len__()  while True:      for thin httprs:          if not th.is_alive():              e -= 1      if e <= 0:          break  print("Multiprocess Http Request", time.time() - t)  </code></pre>    <p>Output: 0.5329999923706055、0.4760000705718994</p>    <h2><strong>实验结果</strong></h2>    <table>     <thead>      <tr>       <th> </th>       <th>CPU密集型操作</th>       <th>IO密集型操作</th>       <th>网络请求密集型操作</th>      </tr>     </thead>     <tbody>      <tr>       <td>线性操作</td>       <td>94.91824996469</td>       <td>22.46199995279</td>       <td>7.3296000004</td>      </tr>      <tr>       <td>多线程操作</td>       <td>101.1700000762</td>       <td>24.8605000973</td>       <td>0.5053332647</td>      </tr>      <tr>       <td>多进程操作</td>       <td>53.8899999857</td>       <td>12.7840000391</td>       <td>0.5045000315</td>      </tr>     </tbody>    </table>    <p>通过上面的结果,我们可以看到:</p>    <ul>     <li>多线程在IO密集型的操作下似乎也没有很大的优势(也许IO操作的任务再繁重一些就能体现出优势),在CPU密集型的操作下明显地比单线程线性执行性能更差,但是对于网络请求这种忙等阻塞线程的操作,多线程的优势便非常显著了</li>     <li>多进程无论是在CPU密集型还是IO密集型以及网络请求密集型(经常发生线程阻塞的操作)中,都能体现出性能的优势。不过在类似网络请求密集型的操作上,与多线程相差无几,但却更占用CPU等资源,所以对于这种情况下,我们可以选择多线程来执行</li>    </ul>    <p style="text-align:center"><br> <img src="https://simg.open-open.com/show/2ee3910aaab0c8c49ffd7bcaf2037493.png"></p>    <p> </p>    <p>来自:http://python.jobbole.com/86822/</p>    <p> </p>