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  <div class="section" id="gpu">
<h1><a class="toc-backref" href="#id9">GPU性能分析与调优</a><a class="headerlink" href="#gpu" title="永久链接至标题"></a></h1>
<div class="contents topic" id="contents">
<p class="topic-title first">Contents</p>
<ul class="simple">
<li><a class="reference internal" href="#gpu" id="id9">GPU性能分析与调优</a><ul>
<li><a class="reference internal" href="#id1" id="id10">什么是性能分析？</a></li>
<li><a class="reference internal" href="#id2" id="id11">为什么需要性能分析？</a></li>
<li><a class="reference internal" href="#id3" id="id12">如何进行性能分析？</a></li>
<li><a class="reference internal" href="#id4" id="id13">性能分析工具介绍</a></li>
<li><a class="reference internal" href="#id5" id="id14">详细教程</a><ul>
<li><a class="reference internal" href="#id6" id="id15">内置定时器</a></li>
<li><a class="reference internal" href="#nvprof" id="id16">nvprof 工具</a></li>
<li><a class="reference internal" href="#nvvp" id="id17">nvvp 工具</a></li>
</ul>
</li>
<li><a class="reference internal" href="#id7" id="id18">性能分析小技巧</a></li>
<li><a class="reference internal" href="#id8" id="id19">参考资料</a></li>
</ul>
</li>
</ul>
</div>
<p>此教程将向您分步介绍如何使用内置的定时工具、 <strong>nvprof</strong> 或 <strong>nvvp</strong> 来运行性能分析和调优。</p>
<ul class="simple">
<li>什么是性能分析？</li>
<li>为什么需要性能分析？</li>
<li>如何进行性能分析？</li>
<li>性能分析工具介绍</li>
<li>详细教程</li>
<li>性能分析小技巧</li>
</ul>
<div class="section" id="id1">
<h2><a class="toc-backref" href="#id10">什么是性能分析？</a><a class="headerlink" href="#id1" title="永久链接至标题"></a></h2>
<p>在软件工程的范畴里，性能分析（Profiling）是一个动态程序分析的术语，它可以指测量一个程序的空间（内存）复杂度或时间复杂度，
也可以说是某些特定指令的使用情况，或者是函数调用的频率和耗时等。通常情况下，分析得到的信息用于协助进行程序的优化。</p>
<p>简单来说，性能分析工具是用于给应用程序的性能做定量分析的。如果想很好的理解程序的行为，那程序分析工具是必不可少的利器。简单的性能分析，可以告诉您某个操作到底花了多长时间？而更深入的分析，甚至能解释为什么某个操作花了很长时间？</p>
</div>
<div class="section" id="id2">
<h2><a class="toc-backref" href="#id11">为什么需要性能分析？</a><a class="headerlink" href="#id2" title="永久链接至标题"></a></h2>
<p>训练好一个深层神经网络通常要耗费非常长的时间，所以性能也就逐步变成了深度学习领域最重要的指标。
而优化性能的首要任务，是需要了解哪些步骤拖慢了整体。
如果某一块根本就不怎么耗时，那也就不需要急着优化性能啦！</p>
</div>
<div class="section" id="id3">
<h2><a class="toc-backref" href="#id12">如何进行性能分析？</a><a class="headerlink" href="#id3" title="永久链接至标题"></a></h2>
<p>为了达到性能最优，您可以采用下面五个步骤：</p>
<ul class="simple">
<li>对代码进行性能分析</li>
<li>找到运行慢的部分</li>
<li>找到运行慢的原因</li>
<li>修改成更快的版本</li>
<li>再次对代码进行性能分析</li>
</ul>
<p>Usually, processor has two key performance limits include float point throughput and
memory throughput. For GPU,  it also need more parallelism to fulfill its potential.
This is why they can be so fast.</p>
<p>通常情况下，处理器有两个关键性能限制：一个是浮点计算量，另一个是内存操作量。
GPU则还需要高并行性，才能发挥其全部能力。这正是它们速度快的原因。</p>
</div>
<div class="section" id="id4">
<h2><a class="toc-backref" href="#id13">性能分析工具介绍</a><a class="headerlink" href="#id4" title="永久链接至标题"></a></h2>
<p>就通常的GPU性能分析来说，市面上已经有NVIDIA或第三方提供的众多工具。</p>
<p><strong>nvprof</strong> 是Nvidia性能分析工具， <strong>nvvp</strong> 则是带GUI的Nvidia可视化性能分析工具。
在这个教程中，我们主要会介绍nvprof和nvvp。</p>
<p><code class="code docutils literal"><span class="pre">test_GpuProfiler</span></code> from <code class="code docutils literal"><span class="pre">paddle/math/tests</span></code> directory will be used to evaluate
above profilers.</p>
<p><code class="code docutils literal"><span class="pre">paddle/math/test</span></code> 目录中的 <code class="code docutils literal"><span class="pre">test_GpuProfiler</span></code> 就是用于展示上述分析工具的用法。</p>
<div class="highlight-c++"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="n">TEST</span><span class="p">(</span><span class="n">Profiler</span><span class="p">,</span> <span class="n">testBilinearFwdBwd</span><span class="p">)</span> <span class="p">{</span>
  <span class="k">auto</span> <span class="n">numSamples</span> <span class="o">=</span> <span class="mi">10</span><span class="p">;</span>
  <span class="k">auto</span> <span class="n">channels</span> <span class="o">=</span> <span class="mi">16</span><span class="p">;</span>
  <span class="k">auto</span> <span class="n">imgSize</span> <span class="o">=</span> <span class="mi">64</span><span class="p">;</span>
  <span class="p">{</span>
    <span class="c1">// nvprof: GPU Proflier</span>
    <span class="n">REGISTER_GPU_PROFILER</span><span class="p">(</span><span class="s">&quot;testBilinearFwdBwd&quot;</span><span class="p">);</span>
    <span class="c1">// Paddle built-in timer</span>
    <span class="n">REGISTER_TIMER_INFO</span><span class="p">(</span>
        <span class="s">&quot;testBilinearFwdBwd&quot;</span><span class="p">,</span>
        <span class="s">&quot;numSamples = 10, channels = 16, imgSizeX = 64, imgSizeY = 64&quot;</span><span class="p">);</span>
    <span class="n">testBilinearFwdBwd</span><span class="p">(</span><span class="n">numSamples</span><span class="p">,</span> <span class="n">imgSize</span><span class="p">,</span> <span class="n">imgSize</span><span class="p">,</span> <span class="n">channels</span><span class="p">);</span>
  <span class="p">}</span>
  <span class="n">globalStat</span><span class="p">.</span><span class="n">printAllStatus</span><span class="p">();</span>
<span class="p">}</span>
</pre></div>
</td></tr></table></div>
<p>上述的代码片段包含了两种方法，您可以任意使用一个或两个来对感兴趣的代码段做性能分析。</p>
<ol class="arabic simple">
<li><code class="code docutils literal"><span class="pre">REGISTER_TIMER_INFO</span></code> 是一个内置的定时器封装，可以用来计算CPU函数或cuda内核的时间消耗。</li>
</ol>
<p>2. <code class="code docutils literal"><span class="pre">REGISTER_GPU_PROFILER</span></code> is a general purpose wrapper object of <code class="code docutils literal"><span class="pre">cudaProfilerStart</span></code> and <code class="code docutils literal"><span class="pre">cudaProfilerStop</span></code> to avoid
program crashes when CPU version of PaddlePaddle invokes them.</p>
<ol class="arabic simple" start="3">
<li><code class="code docutils literal"><span class="pre">REGISTER_GPU_PROFILER</span></code> 是一个封装对象，封装了 <code class="code docutils literal"><span class="pre">cudaProfilerStart</span></code> 和 <code class="code docutils literal"><span class="pre">cudaProfileStop</span></code> 两个操作；同时其内部实现可以避免纯CPU版本PaddlePaddle在执行本语句时发生崩溃。</li>
</ol>
<p>您会在接下来的部分中获得更多的细节介绍。</p>
</div>
<div class="section" id="id5">
<h2><a class="toc-backref" href="#id14">详细教程</a><a class="headerlink" href="#id5" title="永久链接至标题"></a></h2>
<div class="section" id="id6">
<h3><a class="toc-backref" href="#id15">内置定时器</a><a class="headerlink" href="#id6" title="永久链接至标题"></a></h3>
<p>如果想要启用PaddlePaddle的内置定时器，您首先需要在相关代码段中加入 <code class="code docutils literal"><span class="pre">REGISTER_TIMER_INFO</span></code>。
接下来就可以使用 <code class="code docutils literal"><span class="pre">printStatus</span></code> 或者 <code class="code docutils literal"><span class="pre">printAllStatus</span></code> 函数来将信息输出到界面中。
下面举个简单的例子：</p>
<ol class="arabic">
<li><p class="first">加入 <code class="code docutils literal"><span class="pre">REGISTER_TIMER_INFO</span></code> 和 <code class="code docutils literal"><span class="pre">printAllStatus</span></code> 函数（如高亮部分）。</p>
<blockquote>
<div><div class="highlight-c++"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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11
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13
14
15</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="n">TEST</span><span class="p">(</span><span class="n">Profiler</span><span class="p">,</span> <span class="n">testBilinearFwdBwd</span><span class="p">)</span> <span class="p">{</span>
  <span class="k">auto</span> <span class="n">numSamples</span> <span class="o">=</span> <span class="mi">10</span><span class="p">;</span>
  <span class="k">auto</span> <span class="n">channels</span> <span class="o">=</span> <span class="mi">16</span><span class="p">;</span>
  <span class="k">auto</span> <span class="n">imgSize</span> <span class="o">=</span> <span class="mi">64</span><span class="p">;</span>
  <span class="p">{</span>
    <span class="c1">// nvprof: GPU Proflier</span>
    <span class="n">REGISTER_GPU_PROFILER</span><span class="p">(</span><span class="s">&quot;testBilinearFwdBwd&quot;</span><span class="p">);</span>
<span class="hll">    <span class="c1">// Paddle built-in timer</span>
</span><span class="hll">    <span class="n">REGISTER_TIMER_INFO</span><span class="p">(</span>
</span><span class="hll">        <span class="s">&quot;testBilinearFwdBwd&quot;</span><span class="p">,</span>
</span><span class="hll">        <span class="s">&quot;numSamples = 10, channels = 16, imgSizeX = 64, imgSizeY = 64&quot;</span><span class="p">);</span>
</span><span class="hll">    <span class="n">testBilinearFwdBwd</span><span class="p">(</span><span class="n">numSamples</span><span class="p">,</span> <span class="n">imgSize</span><span class="p">,</span> <span class="n">imgSize</span><span class="p">,</span> <span class="n">channels</span><span class="p">);</span>
</span>  <span class="p">}</span>
<span class="hll">  <span class="n">globalStat</span><span class="p">.</span><span class="n">printAllStatus</span><span class="p">();</span>
</span><span class="p">}</span>
</pre></div>
</td></tr></table></div>
</div></blockquote>
</li>
<li><p class="first">cmake配置中将 <strong>WITH_TIMER</strong> 打开，重新编译PaddlePaddle。</p>
<blockquote>
<div><div class="highlight-bash"><div class="highlight"><pre><span></span>cmake .. -DWITH_TIMER<span class="o">=</span>ON
make
</pre></div>
</div>
</div></blockquote>
</li>
<li><p class="first">执行您的代码，并观察结果(如高亮部分）。</p>
<blockquote>
<div><div class="highlight-bash"><div class="highlight"><pre><span></span><span class="hll">&gt; ./paddle/math/tests/test_GpuProfiler
</span>I1117 <span class="m">11</span>:13:42.313065 <span class="m">2522362816</span> Util.cpp:155<span class="o">]</span> commandline: ./paddle/math/tests/test_GpuProfiler
I1117 <span class="m">11</span>:13:42.845065 <span class="m">2522362816</span> Util.cpp:130<span class="o">]</span> Calling runInitFunctions
I1117 <span class="m">11</span>:13:42.845208 <span class="m">2522362816</span> Util.cpp:143<span class="o">]</span> Call runInitFunctions <span class="k">done</span>.
<span class="o">[==========]</span> Running <span class="m">1</span> <span class="nb">test</span> from <span class="m">1</span> <span class="nb">test</span> <span class="k">case</span>.
<span class="o">[</span>----------<span class="o">]</span> Global <span class="nb">test</span> environment set-up.
<span class="o">[</span>----------<span class="o">]</span> <span class="m">1</span> <span class="nb">test</span> from Profiler
<span class="o">[</span> RUN      <span class="o">]</span> Profiler.BilinearFwdBwd
I1117 <span class="m">11</span>:13:42.845310 <span class="m">2522362816</span> test_GpuProfiler.cpp:114<span class="o">]</span> Enable GPU Profiler Stat: <span class="o">[</span>testBilinearFwdBwd<span class="o">]</span> <span class="s2">&quot;numSamples = 10, channels = 16, im</span>
<span class="s2">gSizeX = 64, imgSizeY = 64&quot;</span>
I1117 <span class="m">11</span>:13:42.850154 <span class="m">2522362816</span> ThreadLocal.cpp:37<span class="o">]</span> thread use undeterministic rand seed:20659751
<span class="hll">I1117 <span class="m">11</span>:13:42.981501 <span class="m">2522362816</span> Stat.cpp:130<span class="o">]</span> <span class="o">=======</span> StatSet: <span class="o">[</span>GlobalStatInfo<span class="o">]</span> <span class="nv">status</span> <span class="o">======</span>
</span><span class="hll">I1117 <span class="m">11</span>:13:42.981539 <span class="m">2522362816</span> Stat.cpp:133<span class="o">]</span> <span class="nv">Stat</span><span class="o">=</span>testBilinearFwdBwd     <span class="nv">total</span><span class="o">=</span><span class="m">136</span>.141    <span class="nv">avg</span><span class="o">=</span><span class="m">136</span>.141    <span class="nv">max</span><span class="o">=</span><span class="m">136</span>.141    <span class="nv">min</span><span class="o">=</span><span class="m">136</span>.141   <span class="nv">count</span><span class="o">=</span><span class="m">1</span>
</span><span class="hll">I1117 <span class="m">11</span>:13:42.981572 <span class="m">2522362816</span> Stat.cpp:141<span class="o">]</span> <span class="o">=======</span> BarrierStatSet <span class="nv">status</span> <span class="o">======</span>
</span><span class="hll">I1117 <span class="m">11</span>:13:42.981575 <span class="m">2522362816</span> Stat.cpp:154<span class="o">]</span> --------------------------------------------------
</span><span class="o">[</span>       OK <span class="o">]</span> Profiler.BilinearFwdBwd <span class="o">(</span><span class="m">136</span> ms<span class="o">)</span>
<span class="o">[</span>----------<span class="o">]</span> <span class="m">1</span> <span class="nb">test</span> from Profiler <span class="o">(</span><span class="m">136</span> ms total<span class="o">)</span>

<span class="o">[</span>----------<span class="o">]</span> Global <span class="nb">test</span> environment tear-down
<span class="o">[==========]</span> <span class="m">1</span> <span class="nb">test</span> from <span class="m">1</span> <span class="nb">test</span> <span class="k">case</span> ran. <span class="o">(</span><span class="m">136</span> ms total<span class="o">)</span>
<span class="o">[</span>  PASSED  <span class="o">]</span> <span class="m">1</span> test.
</pre></div>
</div>
</div></blockquote>
</li>
</ol>
</div>
<div class="section" id="nvprof">
<h3><a class="toc-backref" href="#id16">nvprof 工具</a><a class="headerlink" href="#nvprof" title="永久链接至标题"></a></h3>
<p>要使用命令行分析工具 <strong>nvprof</strong>，您按如下步骤操作即可：</p>
<ol class="arabic">
<li><p class="first">将 <code class="code docutils literal"><span class="pre">REGISTER_GPU_PROFILER</span></code> 函数加到代码中（参考强调部分）。</p>
<blockquote>
<div><div class="highlight-c++"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
 2
 3
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 9
10
11
12
13
14
15</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="n">TEST</span><span class="p">(</span><span class="n">Profiler</span><span class="p">,</span> <span class="n">testBilinearFwdBwd</span><span class="p">)</span> <span class="p">{</span>
  <span class="k">auto</span> <span class="n">numSamples</span> <span class="o">=</span> <span class="mi">10</span><span class="p">;</span>
  <span class="k">auto</span> <span class="n">channels</span> <span class="o">=</span> <span class="mi">16</span><span class="p">;</span>
  <span class="k">auto</span> <span class="n">imgSize</span> <span class="o">=</span> <span class="mi">64</span><span class="p">;</span>
  <span class="p">{</span>
<span class="hll">    <span class="c1">// nvprof: GPU Proflier</span>
</span><span class="hll">    <span class="n">REGISTER_GPU_PROFILER</span><span class="p">(</span><span class="s">&quot;testBilinearFwdBwd&quot;</span><span class="p">);</span>
</span>    <span class="c1">// Paddle built-in timer</span>
    <span class="n">REGISTER_TIMER_INFO</span><span class="p">(</span>
        <span class="s">&quot;testBilinearFwdBwd&quot;</span><span class="p">,</span>
        <span class="s">&quot;numSamples = 10, channels = 16, imgSizeX = 64, imgSizeY = 64&quot;</span><span class="p">);</span>
    <span class="n">testBilinearFwdBwd</span><span class="p">(</span><span class="n">numSamples</span><span class="p">,</span> <span class="n">imgSize</span><span class="p">,</span> <span class="n">imgSize</span><span class="p">,</span> <span class="n">channels</span><span class="p">);</span>
  <span class="p">}</span>
  <span class="n">globalStat</span><span class="p">.</span><span class="n">printAllStatus</span><span class="p">();</span>
<span class="p">}</span>
</pre></div>
</td></tr></table></div>
</div></blockquote>
</li>
<li><p class="first">cmake中将 <strong>WITH_PROFILER</strong> 配置打开，重新编译PaddlePaddle。</p>
<blockquote>
<div><div class="highlight-bash"><div class="highlight"><pre><span></span>cmake .. -DWITH_PROFILER<span class="o">=</span>ON
make
</pre></div>
</div>
</div></blockquote>
</li>
<li><p class="first">使用 <strong>nvprof</strong> 来分析执行文件。</p>
<blockquote>
<div><div class="highlight-bash"><div class="highlight"><pre><span></span>nvprof  ./paddle/math/tests/test_GpuProfiler
</pre></div>
</div>
</div></blockquote>
</li>
</ol>
<p>然后，您就能获得如下的分析结果：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="o">==</span><span class="nv">78544</span><span class="o">==</span> Profiling application: ./paddle/math/tests/test_GpuProfiler
<span class="o">==</span><span class="nv">78544</span><span class="o">==</span> Profiling result:
Time<span class="o">(</span>%<span class="o">)</span>     Time     Calls       Avg       Min       Max  Name
<span class="m">27</span>.60%  <span class="m">9</span>.6305ms         <span class="m">5</span>  <span class="m">1</span>.9261ms  <span class="m">3</span>.4560us  <span class="m">6</span>.4035ms  <span class="o">[</span>CUDA memcpy HtoD<span class="o">]</span>
<span class="m">26</span>.07%  <span class="m">9</span>.0957ms         <span class="m">1</span>  <span class="m">9</span>.0957ms  <span class="m">9</span>.0957ms  <span class="m">9</span>.0957ms  KeBilinearInterpBw
<span class="m">23</span>.78%  <span class="m">8</span>.2977ms         <span class="m">1</span>  <span class="m">8</span>.2977ms  <span class="m">8</span>.2977ms  <span class="m">8</span>.2977ms  KeBilinearInterpFw
<span class="m">22</span>.55%  <span class="m">7</span>.8661ms         <span class="m">2</span>  <span class="m">3</span>.9330ms  <span class="m">1</span>.5798ms  <span class="m">6</span>.2863ms  <span class="o">[</span>CUDA memcpy DtoH<span class="o">]</span>

<span class="o">==</span><span class="nv">78544</span><span class="o">==</span> API calls:
Time<span class="o">(</span>%<span class="o">)</span>     Time     Calls       Avg       Min       Max  Name
<span class="m">46</span>.85%  <span class="m">682</span>.28ms         <span class="m">8</span>  <span class="m">85</span>.285ms  <span class="m">12</span>.639us  <span class="m">682</span>.03ms  cudaStreamCreateWithFlags
<span class="m">39</span>.83%  <span class="m">580</span>.00ms         <span class="m">4</span>  <span class="m">145</span>.00ms     302ns  <span class="m">550</span>.27ms  cudaFree
<span class="m">9</span>.82%   <span class="m">143</span>.03ms         <span class="m">9</span>  <span class="m">15</span>.892ms  <span class="m">8</span>.7090us  <span class="m">142</span>.78ms  cudaStreamCreate
<span class="m">1</span>.23%   <span class="m">17</span>.983ms         <span class="m">7</span>  <span class="m">2</span>.5690ms  <span class="m">23</span>.210us  <span class="m">6</span>.4563ms  cudaMemcpy
<span class="m">1</span>.23%   <span class="m">17</span>.849ms         <span class="m">2</span>  <span class="m">8</span>.9247ms  <span class="m">8</span>.4726ms  <span class="m">9</span>.3768ms  cudaStreamSynchronize
<span class="m">0</span>.66%   <span class="m">9</span>.5969ms         <span class="m">7</span>  <span class="m">1</span>.3710ms  <span class="m">288</span>.43us  <span class="m">2</span>.4279ms  cudaHostAlloc
<span class="m">0</span>.13%   <span class="m">1</span>.9530ms        <span class="m">11</span>  <span class="m">177</span>.54us  <span class="m">7</span>.6810us  <span class="m">591</span>.06us  cudaMalloc
<span class="m">0</span>.07%   <span class="m">1</span>.0424ms         <span class="m">8</span>  <span class="m">130</span>.30us  <span class="m">1</span>.6970us  <span class="m">453</span>.72us  cudaGetDevice
<span class="m">0</span>.04%   <span class="m">527</span>.90us        <span class="m">40</span>  <span class="m">13</span>.197us     525ns  <span class="m">253</span>.99us  cudaEventCreateWithFlags
<span class="m">0</span>.03%   <span class="m">435</span>.73us       <span class="m">348</span>  <span class="m">1</span>.2520us     124ns  <span class="m">42</span>.704us  cuDeviceGetAttribute
<span class="m">0</span>.03%   <span class="m">419</span>.36us         <span class="m">1</span>  <span class="m">419</span>.36us  <span class="m">419</span>.36us  <span class="m">419</span>.36us  cudaGetDeviceCount
<span class="m">0</span>.02%   <span class="m">260</span>.75us         <span class="m">2</span>  <span class="m">130</span>.38us  <span class="m">129</span>.32us  <span class="m">131</span>.43us  cudaGetDeviceProperties
<span class="m">0</span>.02%   <span class="m">222</span>.32us         <span class="m">2</span>  <span class="m">111</span>.16us  <span class="m">106</span>.94us  <span class="m">115</span>.39us  cudaLaunch
<span class="m">0</span>.01%   <span class="m">214</span>.06us         <span class="m">4</span>  <span class="m">53</span>.514us  <span class="m">28</span>.586us  <span class="m">77</span>.655us  cuDeviceGetName
<span class="m">0</span>.01%   <span class="m">115</span>.45us         <span class="m">4</span>  <span class="m">28</span>.861us  <span class="m">9</span>.8250us  <span class="m">44</span>.526us  cuDeviceTotalMem
<span class="m">0</span>.01%   <span class="m">83</span>.988us         <span class="m">4</span>  <span class="m">20</span>.997us     578ns  <span class="m">77</span>.760us  cudaSetDevice
<span class="m">0</span>.00%   <span class="m">38</span>.918us         <span class="m">1</span>  <span class="m">38</span>.918us  <span class="m">38</span>.918us  <span class="m">38</span>.918us  cudaEventCreate
<span class="m">0</span>.00%   <span class="m">34</span>.573us        <span class="m">31</span>  <span class="m">1</span>.1150us     279ns  <span class="m">12</span>.784us  cudaDeviceGetAttribute
<span class="m">0</span>.00%   <span class="m">17</span>.767us         <span class="m">1</span>  <span class="m">17</span>.767us  <span class="m">17</span>.767us  <span class="m">17</span>.767us  cudaProfilerStart
<span class="m">0</span>.00%   <span class="m">15</span>.228us         <span class="m">2</span>  <span class="m">7</span>.6140us  <span class="m">3</span>.5460us  <span class="m">11</span>.682us  cudaConfigureCall
<span class="m">0</span>.00%   <span class="m">14</span>.536us         <span class="m">2</span>  <span class="m">7</span>.2680us  <span class="m">1</span>.1490us  <span class="m">13</span>.387us  cudaGetLastError
<span class="m">0</span>.00%   <span class="m">8</span>.6080us        <span class="m">26</span>     331ns     173ns     783ns  cudaSetupArgument
<span class="m">0</span>.00%   <span class="m">5</span>.5470us         <span class="m">6</span>     924ns     215ns  <span class="m">2</span>.6780us  cuDeviceGet
<span class="m">0</span>.00%   <span class="m">5</span>.4090us         <span class="m">6</span>     901ns     328ns  <span class="m">3</span>.3320us  cuDeviceGetCount
<span class="m">0</span>.00%   <span class="m">4</span>.1770us         <span class="m">3</span>  <span class="m">1</span>.3920us  <span class="m">1</span>.0630us  <span class="m">1</span>.8300us  cuDriverGetVersion
<span class="m">0</span>.00%   <span class="m">3</span>.4650us         <span class="m">3</span>  <span class="m">1</span>.1550us  <span class="m">1</span>.0810us  <span class="m">1</span>.2680us  cuInit
<span class="m">0</span>.00%      830ns         <span class="m">1</span>     830ns     830ns     830ns  cudaRuntimeGetVersion
</pre></div>
</div>
</div>
<div class="section" id="nvvp">
<h3><a class="toc-backref" href="#id17">nvvp 工具</a><a class="headerlink" href="#nvvp" title="永久链接至标题"></a></h3>
<p>如果想使用可视化的分析器 <strong>nvvp</strong>，您可以导入 <code class="code docutils literal"><span class="pre">nvprof</span> <span class="pre">-o</span> <span class="pre">...</span></code> 的输出，或者从工具的界面里运行您的应用。</p>
<p><strong>备注: nvvp 也支持CPU的性能分析</strong> (需在nvvp界面中选上才能开启）</p>
<a class="reference internal image-reference" href="../../_images/nvvp1.png"><img alt="../../_images/nvvp1.png" class="align-center" src="../../_images/nvvp1.png" style="width: 932.58px; height: 399.3px;" /></a>
<p>从内核函数的角度， <strong>nvvp</strong> 可以精确说明一个长耗时操作的具体原因。
同时，如下图所示， <strong>nvvp</strong> 的内核block使用情况、寄存器使用情况和共享内存使用情况能让我们对GPU的整体使用有更好的理解。</p>
<a class="reference internal image-reference" href="../../_images/nvvp2.png"><img alt="../../_images/nvvp2.png" class="align-center" src="../../_images/nvvp2.png" style="width: 727.32px; height: 475.86px;" /></a>
<p>而从应用的角度， <strong>nvvp</strong> 可以帮您提供一些定位性能瓶颈的建议。
例如，下图中就展示了一些关于内存数据迁徙和计算资源利用率的建议，为您做性能调优提供了方向。</p>
<a class="reference internal image-reference" href="../../_images/nvvp3.png"><img alt="../../_images/nvvp3.png" class="align-center" src="../../_images/nvvp3.png" style="width: 717.42px; height: 213.84px;" /></a>
<a class="reference internal image-reference" href="../../_images/nvvp4.png"><img alt="../../_images/nvvp4.png" class="align-center" src="../../_images/nvvp4.png" style="width: 718.08px; height: 197.34px;" /></a>
</div>
</div>
<div class="section" id="id7">
<h2><a class="toc-backref" href="#id18">性能分析小技巧</a><a class="headerlink" href="#id7" title="永久链接至标题"></a></h2>
<ul>
<li><p class="first">开始阶段，从 <strong>nvprof</strong> 和 <strong>nvvp</strong> 的输出信息入手是个不错的选择。</p>
</li>
<li><p class="first">接下来可以考虑下时间线的分析。</p>
</li>
<li><p class="first">如果真想挖掘内核深处的某个秘密，您最好先确认：这一块的耗时比例真的太高，值得深入分析。</p>
</li>
<li><p class="first">可能的情况下，试着让输出的分析数据和理论值对应。</p>
<blockquote>
<div><ol class="arabic simple">
<li>例如，如果我知道内核花了10ms来移动1GB数据，那我会期望分析工具统计到速度是100GB/s。</li>
<li>若有不一致之处，很有可能实际应用就是没有按照您的预期情况运行。</li>
</ol>
</div></blockquote>
</li>
<li><p class="first">了解您的硬件：如果您的GPU理论可以达到6 TFLOPs（6万亿次浮点运算每秒），而当前已经有5.5 TFLOPs了，那估计这里的潜力就没啥好挖的了……</p>
</li>
</ul>
<p>性能分析是性能优化的关键一步。有的时候简简单单的改变就能在性能上产生明显的优化效果！
当然，具体情况因人而异。</p>
</div>
<div class="section" id="id8">
<h2><a class="toc-backref" href="#id19">参考资料</a><a class="headerlink" href="#id8" title="永久链接至标题"></a></h2>
<p>Jeremy Appleyard, <a class="reference external" href="http://www.robots.ox.ac.uk/~seminars/seminars/Extra/2015_10_08_JeremyAppleyard.pdf">GPU Profiling for Deep Learning</a>, 2015</p>
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