From 909c2a7f4191bf2e1626ef941d47ef5d3fc0561d Mon Sep 17 00:00:00 2001
From: Travis CI <paddle-dev@baidu.com>
Date: Mon, 27 Nov 2017 04:57:16 +0000
Subject: [PATCH] Deploy to GitHub Pages:
 7c57f90dc2e95704e71adc40931581d1be437f32

---
 develop/doc/_images/pprof_1.png               | Bin 0 -> 352710 bytes
 develop/doc/_images/pprof_2.png               | Bin 0 -> 194000 bytes
 .../howto/optimization/cpu_profiling.md.txt   | 163 ++++++++
 .../doc/howto/optimization/cpu_profiling.html | 375 +++++++++++++++++
 develop/doc/objects.inv                       | Bin 3091 -> 3130 bytes
 develop/doc/searchindex.js                    |   2 +-
 develop/doc_cn/_images/pprof_1.png            | Bin 0 -> 352710 bytes
 develop/doc_cn/_images/pprof_2.png            | Bin 0 -> 194000 bytes
 .../howto/optimization/cpu_profiling.md.txt   | 163 ++++++++
 .../howto/optimization/cpu_profiling.html     | 390 ++++++++++++++++++
 develop/doc_cn/objects.inv                    | Bin 3618 -> 3634 bytes
 develop/doc_cn/searchindex.js                 |   2 +-
 12 files changed, 1093 insertions(+), 2 deletions(-)
 create mode 100644 develop/doc/_images/pprof_1.png
 create mode 100644 develop/doc/_images/pprof_2.png
 create mode 100644 develop/doc/_sources/howto/optimization/cpu_profiling.md.txt
 create mode 100644 develop/doc/howto/optimization/cpu_profiling.html
 create mode 100644 develop/doc_cn/_images/pprof_1.png
 create mode 100644 develop/doc_cn/_images/pprof_2.png
 create mode 100644 develop/doc_cn/_sources/howto/optimization/cpu_profiling.md.txt
 create mode 100644 develop/doc_cn/howto/optimization/cpu_profiling.html

diff --git a/develop/doc/_images/pprof_1.png b/develop/doc/_images/pprof_1.png
new file mode 100644
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diff --git a/develop/doc/_sources/howto/optimization/cpu_profiling.md.txt b/develop/doc/_sources/howto/optimization/cpu_profiling.md.txt
new file mode 100644
index 00000000000..32d89a7c183
--- /dev/null
+++ b/develop/doc/_sources/howto/optimization/cpu_profiling.md.txt
@@ -0,0 +1,163 @@
+此教程会介绍如何使用Python的cProfile包，与Python库yep，google perftools来运行性能分析(Profiling)与调优。
+
+运行性能分析可以让开发人员科学的，有条不紊的对程序进行性能优化。性能分析是性能调优的基础。因为在程序实际运行中，真正的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。
+
+性能优化的步骤，通常是循环重复若干次『性能分析 --> 寻找瓶颈 ---> 调优瓶颈 --> 性能分析确认调优效果』。其中性能分析是性能调优的至关重要的量化指标。
+
+Paddle提供了Python语言绑定。用户使用Python进行神经网络编程，训练，测试。Python解释器通过`pybind`和`swig`调用Paddle的动态链接库，进而调用Paddle C++部分的代码。所以Paddle的性能分析与调优分为两个部分:
+
+* Python代码的性能分析
+* Python与C++混合代码的性能分析
+
+
+## Python代码的性能分析
+
+### 生成性能分析文件
+
+Python标准库中提供了性能分析的工具包，[cProfile](https://docs.python.org/2/library/profile.html)。生成Python性能分析的命令如下:
+
+```bash
+python -m cProfile -o profile.out main.py
+```
+
+其中`-o`标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，`cProfile`会打印一些统计信息到`stdout`。这不方便我们进行后期处理(进行`sort`, `split`, `cut`等等)。
+
+### 查看性能分析文件
+
+当main.py运行完毕后，性能分析结果文件`profile.out`就生成出来了。我们可以使用[cprofilev](https://github.com/ymichael/cprofilev)来查看性能分析结果。`cprofilev`是一个Python的第三方库。使用它会开启一个HTTP服务，将性能分析结果以网页的形式展示出来。
+
+使用`pip install cprofilev`安装`cprofilev`工具。安装完成后，使用如下命令开启HTTP服务
+
+```bash
+cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
+```
+
+其中`-a`标识HTTP服务绑定的IP。使用`0.0.0.0`允许外网访问这个HTTP服务。`-p`标识HTTP服务的端口。`-f`标识性能分析的结果文件。`main.py`标识被性能分析的源文件。
+
+访问对应网址，即可显示性能分析的结果。性能分析结果格式如下:
+
+```text
+   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
+        1    0.284    0.284   29.514   29.514 main.py:1(<module>)
+     4696    0.128    0.000   15.748    0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/executor.py:20(run)
+     4696   12.040    0.003   12.040    0.003 {built-in method run}
+        1    0.144    0.144    6.534    6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14(<module>)
+```
+
+每一列的含义是:
+
+| 列名 | 含义 |
+| --- | --- |
+| ncalls | 函数的调用次数 |
+| tottime | 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间 |
+| percall | tottime的每次调用平均时间 |
+| cumtime | 函数总时间。包含这个函数调用其他函数的时间 |
+| percall | cumtime的每次调用平均时间 |
+| filename:lineno(function) | 文件名, 行号，函数名 |
+
+
+### 寻找性能瓶颈
+
+通常`tottime`和`cumtime`是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。
+
+将性能分析结果按照tottime排序，效果如下:
+
+```text
+     4696   12.040    0.003   12.040    0.003 {built-in method run}
+   300005    0.874    0.000    1.681    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader)
+   107991    0.676    0.000    1.519    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:219(__init__)
+     4697    0.626    0.000    2.291    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)
+        1    0.618    0.618    0.618    0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/__init__.py:1(<module>)
+
+```
+
+可以看到最耗时的函数是C++端的`run`函数。这需要联合我们第二节`Python与C++混合代码的性能分析`来进行调优。而`sync_with_cpp`函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击`sync_with_cpp`的详细信息，了解其调用关系。
+
+```text
+Called By:
+
+   Ordered by: internal time
+   List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
+
+Function                                                                                                 was called by...
+                                                                                                             ncalls  tottime  cumtime
+/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)  <-    4697    0.626    2.291  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)
+/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)  <-    4696    0.019    2.316  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:487(clone)
+                                                                                                                  1    0.000    0.001  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:534(append_backward)
+
+
+Called:
+
+   Ordered by: internal time
+   List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
+```
+
+通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。
+
+
+
+## Python与C++混合代码的性能分析
+
+### 生成性能分析文件
+
+C++的性能分析工具非常多。常见的包括`gprof`, `valgrind`, `google-perftools`。但是调试Python中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而Python的一个第三方库`yep`提供了方便的和`google-perftools`交互的方法。于是这里使用`yep`进行Python与C++混合代码的性能分析
+
+使用`yep`前需要安装`google-perftools`与`yep`包。ubuntu下安装命令为
+
+```bash
+apt install libgoogle-perftools-dev
+pip install yep
+```
+
+安装完毕后，我们可以通过
+
+```bash
+python -m yep -v main.py
+```
+
+生成性能分析文件。生成的性能分析文件为`main.py.prof`。
+
+命令行中的`-v`指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为C++与Python不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施:
+
+1. 编译时指定`-g`生成调试信息。使用cmake的话，可以将CMAKE_BUILD_TYPE指定为`RelWithDebInfo`。
+2. 编译时一定要开启优化。单纯的`Debug`编译性能会和`-O2`或者`-O3`有非常大的差别。`Debug`模式下的性能测试是没有意义的。
+3. 运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟如果单线程调试更容易。可以设置`OMP_NUM_THREADS=1`这个环境变量关闭openmp优化。
+
+### 查看性能分析文件
+
+在运行完性能分析后，会生成性能分析结果文件。我们可以使用[pprof](https://github.com/google/pprof)来显示性能分析结果。注意，这里使用了用`Go`语言重构后的`pprof`，因为这个工具具有web服务界面，且展示效果更好。
+
+安装`pprof`的命令和一般的`Go`程序是一样的，其命令如下:
+
+```bash
+go get github.com/google/pprof
+```
+
+进而我们可以使用如下命令开启一个HTTP服务:
+
+```bash
+pprof -http=0.0.0.0:3213 `which python`  ./main.py.prof
+```
+
+这行命令中，`-http`指开启HTTP服务。`which python`会产生当前Python二进制的完整路径，进而指定了Python可执行文件的路径。`./main.py.prof`输入了性能分析结果。
+
+访问对应的网址，我们可以查看性能分析的结果。结果如下图所示:
+
+![result](./pprof_1.png)
+
+
+### 寻找性能瓶颈
+
+与寻找Python代码的性能瓶颈类似，寻找Python与C++混合代码的性能瓶颈也是要看`tottime`和`cumtime`。而`pprof`展示的调用图也可以帮助我们发现性能中的问题。
+
+例如下图中，
+
+![kernel_perf](./pprof_2.png)
+
+在一次训练中，乘法和乘法梯度的计算占用2%-4%左右的计算时间。而`MomentumOp`占用了17%左右的计算时间。显然，`MomentumOp`的性能有问题。
+
+在`pprof`中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。
+
+## 总结
+
+至此，两种性能分析的方式都介绍完毕了。希望通过这两种性能分析的方式，Paddle的开发人员和使用人员可以有次序的，科学的发现和解决性能问题。
diff --git a/develop/doc/howto/optimization/cpu_profiling.html b/develop/doc/howto/optimization/cpu_profiling.html
new file mode 100644
index 00000000000..b40d544b496
--- /dev/null
+++ b/develop/doc/howto/optimization/cpu_profiling.html
@@ -0,0 +1,375 @@
+
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+  <ul class="wy-breadcrumbs">
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+    <li>Python代码的性能分析</li>
+  </ul>
+</div>
+      
+      <div class="wy-nav-content" id="doc-content">
+        <div class="rst-content">
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  <p>此教程会介绍如何使用Python的cProfile包，与Python库yep，google perftools来运行性能分析(Profiling)与调优。</p>
+<p>运行性能分析可以让开发人员科学的，有条不紊的对程序进行性能优化。性能分析是性能调优的基础。因为在程序实际运行中，真正的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。</p>
+<p>性能优化的步骤，通常是循环重复若干次『性能分析 &#8211;&gt; 寻找瓶颈 &#8212;&gt; 调优瓶颈 &#8211;&gt; 性能分析确认调优效果』。其中性能分析是性能调优的至关重要的量化指标。</p>
+<p>Paddle提供了Python语言绑定。用户使用Python进行神经网络编程，训练，测试。Python解释器通过<code class="docutils literal"><span class="pre">pybind</span></code>和<code class="docutils literal"><span class="pre">swig</span></code>调用Paddle的动态链接库，进而调用Paddle C++部分的代码。所以Paddle的性能分析与调优分为两个部分:</p>
+<ul class="simple">
+<li>Python代码的性能分析</li>
+<li>Python与C++混合代码的性能分析</li>
+</ul>
+<div class="section" id="python">
+<span id="python"></span><h1>Python代码的性能分析<a class="headerlink" href="#python" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="">
+<span id="id1"></span><h2>生成性能分析文件<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
+<p>Python标准库中提供了性能分析的工具包，<a class="reference external" href="https://docs.python.org/2/library/profile.html">cProfile</a>。生成Python性能分析的命令如下:</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>python -m cProfile -o profile.out main.py
+</pre></div>
+</div>
+<p>其中<code class="docutils literal"><span class="pre">-o</span></code>标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，<code class="docutils literal"><span class="pre">cProfile</span></code>会打印一些统计信息到<code class="docutils literal"><span class="pre">stdout</span></code>。这不方便我们进行后期处理(进行<code class="docutils literal"><span class="pre">sort</span></code>, <code class="docutils literal"><span class="pre">split</span></code>, <code class="docutils literal"><span class="pre">cut</span></code>等等)。</p>
+</div>
+<div class="section" id="">
+<span id="id2"></span><h2>查看性能分析文件<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
+<p>当main.py运行完毕后，性能分析结果文件<code class="docutils literal"><span class="pre">profile.out</span></code>就生成出来了。我们可以使用<a class="reference external" href="https://github.com/ymichael/cprofilev">cprofilev</a>来查看性能分析结果。<code class="docutils literal"><span class="pre">cprofilev</span></code>是一个Python的第三方库。使用它会开启一个HTTP服务，将性能分析结果以网页的形式展示出来。</p>
+<p>使用<code class="docutils literal"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">cprofilev</span></code>安装<code class="docutils literal"><span class="pre">cprofilev</span></code>工具。安装完成后，使用如下命令开启HTTP服务</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>cprofilev -a <span class="m">0</span>.0.0.0 -p <span class="m">3214</span> -f profile.out main.py
+</pre></div>
+</div>
+<p>其中<code class="docutils literal"><span class="pre">-a</span></code>标识HTTP服务绑定的IP。使用<code class="docutils literal"><span class="pre">0.0.0.0</span></code>允许外网访问这个HTTP服务。<code class="docutils literal"><span class="pre">-p</span></code>标识HTTP服务的端口。<code class="docutils literal"><span class="pre">-f</span></code>标识性能分析的结果文件。<code class="docutils literal"><span class="pre">main.py</span></code>标识被性能分析的源文件。</p>
+<p>访问对应网址，即可显示性能分析的结果。性能分析结果格式如下:</p>
+<div class="highlight-text"><div class="highlight"><pre><span></span>   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
+        1    0.284    0.284   29.514   29.514 main.py:1(&lt;module&gt;)
+     4696    0.128    0.000   15.748    0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/executor.py:20(run)
+     4696   12.040    0.003   12.040    0.003 {built-in method run}
+        1    0.144    0.144    6.534    6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14(&lt;module&gt;)
+</pre></div>
+</div>
+<p>每一列的含义是:</p>
+<p>| 列名 | 含义 |
+| &#8212; | &#8212; |
+| ncalls | 函数的调用次数 |
+| tottime | 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间 |
+| percall | tottime的每次调用平均时间 |
+| cumtime | 函数总时间。包含这个函数调用其他函数的时间 |
+| percall | cumtime的每次调用平均时间 |
+| filename:lineno(function) | 文件名, 行号，函数名 |</p>
+</div>
+<div class="section" id="">
+<span id="id3"></span><h2>寻找性能瓶颈<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
+<p>通常<code class="docutils literal"><span class="pre">tottime</span></code>和<code class="docutils literal"><span class="pre">cumtime</span></code>是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。</p>
+<p>将性能分析结果按照tottime排序，效果如下:</p>
+<div class="highlight-text"><div class="highlight"><pre><span></span>     4696   12.040    0.003   12.040    0.003 {built-in method run}
+   300005    0.874    0.000    1.681    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader)
+   107991    0.676    0.000    1.519    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:219(__init__)
+     4697    0.626    0.000    2.291    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)
+        1    0.618    0.618    0.618    0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/__init__.py:1(&lt;module&gt;)
+</pre></div>
+</div>
+<p>可以看到最耗时的函数是C++端的<code class="docutils literal"><span class="pre">run</span></code>函数。这需要联合我们第二节<code class="docutils literal"><span class="pre">Python与C++混合代码的性能分析</span></code>来进行调优。而<code class="docutils literal"><span class="pre">sync_with_cpp</span></code>函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击<code class="docutils literal"><span class="pre">sync_with_cpp</span></code>的详细信息，了解其调用关系。</p>
+<div class="highlight-text"><div class="highlight"><pre><span></span>Called By:
+
+   Ordered by: internal time
+   List reduced from 4497 to 2 due to restriction &lt;&#39;sync_with_cpp&#39;&gt;
+
+Function                                                                                                 was called by...
+                                                                                                             ncalls  tottime  cumtime
+/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)  &lt;-    4697    0.626    2.291  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)
+/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)  &lt;-    4696    0.019    2.316  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:487(clone)
+                                                                                                                  1    0.000    0.001  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:534(append_backward)
+
+
+Called:
+
+   Ordered by: internal time
+   List reduced from 4497 to 2 due to restriction &lt;&#39;sync_with_cpp&#39;&gt;
+</pre></div>
+</div>
+<p>通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。</p>
+</div>
+</div>
+<div class="section" id="pythonc">
+<span id="pythonc"></span><h1>Python与C++混合代码的性能分析<a class="headerlink" href="#pythonc" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="">
+<span id="id4"></span><h2>生成性能分析文件<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
+<p>C++的性能分析工具非常多。常见的包括<code class="docutils literal"><span class="pre">gprof</span></code>, <code class="docutils literal"><span class="pre">valgrind</span></code>, <code class="docutils literal"><span class="pre">google-perftools</span></code>。但是调试Python中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而Python的一个第三方库<code class="docutils literal"><span class="pre">yep</span></code>提供了方便的和<code class="docutils literal"><span class="pre">google-perftools</span></code>交互的方法。于是这里使用<code class="docutils literal"><span class="pre">yep</span></code>进行Python与C++混合代码的性能分析</p>
+<p>使用<code class="docutils literal"><span class="pre">yep</span></code>前需要安装<code class="docutils literal"><span class="pre">google-perftools</span></code>与<code class="docutils literal"><span class="pre">yep</span></code>包。ubuntu下安装命令为</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>apt install libgoogle-perftools-dev
+pip install yep
+</pre></div>
+</div>
+<p>安装完毕后，我们可以通过</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>python -m yep -v main.py
+</pre></div>
+</div>
+<p>生成性能分析文件。生成的性能分析文件为<code class="docutils literal"><span class="pre">main.py.prof</span></code>。</p>
+<p>命令行中的<code class="docutils literal"><span class="pre">-v</span></code>指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为C++与Python不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施:</p>
+<ol class="simple">
+<li>编译时指定<code class="docutils literal"><span class="pre">-g</span></code>生成调试信息。使用cmake的话，可以将CMAKE_BUILD_TYPE指定为<code class="docutils literal"><span class="pre">RelWithDebInfo</span></code>。</li>
+<li>编译时一定要开启优化。单纯的<code class="docutils literal"><span class="pre">Debug</span></code>编译性能会和<code class="docutils literal"><span class="pre">-O2</span></code>或者<code class="docutils literal"><span class="pre">-O3</span></code>有非常大的差别。<code class="docutils literal"><span class="pre">Debug</span></code>模式下的性能测试是没有意义的。</li>
+<li>运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟如果单线程调试更容易。可以设置<code class="docutils literal"><span class="pre">OMP_NUM_THREADS=1</span></code>这个环境变量关闭openmp优化。</li>
+</ol>
+</div>
+<div class="section" id="">
+<span id="id5"></span><h2>查看性能分析文件<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
+<p>在运行完性能分析后，会生成性能分析结果文件。我们可以使用<a class="reference external" href="https://github.com/google/pprof">pprof</a>来显示性能分析结果。注意，这里使用了用<code class="docutils literal"><span class="pre">Go</span></code>语言重构后的<code class="docutils literal"><span class="pre">pprof</span></code>，因为这个工具具有web服务界面，且展示效果更好。</p>
+<p>安装<code class="docutils literal"><span class="pre">pprof</span></code>的命令和一般的<code class="docutils literal"><span class="pre">Go</span></code>程序是一样的，其命令如下:</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>go get github.com/google/pprof
+</pre></div>
+</div>
+<p>进而我们可以使用如下命令开启一个HTTP服务:</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>pprof -http<span class="o">=</span><span class="m">0</span>.0.0.0:3213 <span class="sb">`</span>which python<span class="sb">`</span>  ./main.py.prof
+</pre></div>
+</div>
+<p>这行命令中，<code class="docutils literal"><span class="pre">-http</span></code>指开启HTTP服务。<code class="docutils literal"><span class="pre">which</span> <span class="pre">python</span></code>会产生当前Python二进制的完整路径，进而指定了Python可执行文件的路径。<code class="docutils literal"><span class="pre">./main.py.prof</span></code>输入了性能分析结果。</p>
+<p>访问对应的网址，我们可以查看性能分析的结果。结果如下图所示:</p>
+<p><img alt="result" src="../../_images/pprof_1.png" /></p>
+</div>
+<div class="section" id="">
+<span id="id6"></span><h2>寻找性能瓶颈<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
+<p>与寻找Python代码的性能瓶颈类似，寻找Python与C++混合代码的性能瓶颈也是要看<code class="docutils literal"><span class="pre">tottime</span></code>和<code class="docutils literal"><span class="pre">cumtime</span></code>。而<code class="docutils literal"><span class="pre">pprof</span></code>展示的调用图也可以帮助我们发现性能中的问题。</p>
+<p>例如下图中，</p>
+<p><img alt="kernel_perf" src="../../_images/pprof_2.png" /></p>
+<p>在一次训练中，乘法和乘法梯度的计算占用2%-4%左右的计算时间。而<code class="docutils literal"><span class="pre">MomentumOp</span></code>占用了17%左右的计算时间。显然，<code class="docutils literal"><span class="pre">MomentumOp</span></code>的性能有问题。</p>
+<p>在<code class="docutils literal"><span class="pre">pprof</span></code>中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。</p>
+</div>
+</div>
+<div class="section" id="">
+<span id="id7"></span><h1>总结<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h1>
+<p>至此，两种性能分析的方式都介绍完毕了。希望通过这两种性能分析的方式，Paddle的开发人员和使用人员可以有次序的，科学的发现和解决性能问题。</p>
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diff --git a/develop/doc_cn/_sources/howto/optimization/cpu_profiling.md.txt b/develop/doc_cn/_sources/howto/optimization/cpu_profiling.md.txt
new file mode 100644
index 00000000000..32d89a7c183
--- /dev/null
+++ b/develop/doc_cn/_sources/howto/optimization/cpu_profiling.md.txt
@@ -0,0 +1,163 @@
+此教程会介绍如何使用Python的cProfile包，与Python库yep，google perftools来运行性能分析(Profiling)与调优。
+
+运行性能分析可以让开发人员科学的，有条不紊的对程序进行性能优化。性能分析是性能调优的基础。因为在程序实际运行中，真正的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。
+
+性能优化的步骤，通常是循环重复若干次『性能分析 --> 寻找瓶颈 ---> 调优瓶颈 --> 性能分析确认调优效果』。其中性能分析是性能调优的至关重要的量化指标。
+
+Paddle提供了Python语言绑定。用户使用Python进行神经网络编程，训练，测试。Python解释器通过`pybind`和`swig`调用Paddle的动态链接库，进而调用Paddle C++部分的代码。所以Paddle的性能分析与调优分为两个部分:
+
+* Python代码的性能分析
+* Python与C++混合代码的性能分析
+
+
+## Python代码的性能分析
+
+### 生成性能分析文件
+
+Python标准库中提供了性能分析的工具包，[cProfile](https://docs.python.org/2/library/profile.html)。生成Python性能分析的命令如下:
+
+```bash
+python -m cProfile -o profile.out main.py
+```
+
+其中`-o`标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，`cProfile`会打印一些统计信息到`stdout`。这不方便我们进行后期处理(进行`sort`, `split`, `cut`等等)。
+
+### 查看性能分析文件
+
+当main.py运行完毕后，性能分析结果文件`profile.out`就生成出来了。我们可以使用[cprofilev](https://github.com/ymichael/cprofilev)来查看性能分析结果。`cprofilev`是一个Python的第三方库。使用它会开启一个HTTP服务，将性能分析结果以网页的形式展示出来。
+
+使用`pip install cprofilev`安装`cprofilev`工具。安装完成后，使用如下命令开启HTTP服务
+
+```bash
+cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
+```
+
+其中`-a`标识HTTP服务绑定的IP。使用`0.0.0.0`允许外网访问这个HTTP服务。`-p`标识HTTP服务的端口。`-f`标识性能分析的结果文件。`main.py`标识被性能分析的源文件。
+
+访问对应网址，即可显示性能分析的结果。性能分析结果格式如下:
+
+```text
+   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
+        1    0.284    0.284   29.514   29.514 main.py:1(<module>)
+     4696    0.128    0.000   15.748    0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/executor.py:20(run)
+     4696   12.040    0.003   12.040    0.003 {built-in method run}
+        1    0.144    0.144    6.534    6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14(<module>)
+```
+
+每一列的含义是:
+
+| 列名 | 含义 |
+| --- | --- |
+| ncalls | 函数的调用次数 |
+| tottime | 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间 |
+| percall | tottime的每次调用平均时间 |
+| cumtime | 函数总时间。包含这个函数调用其他函数的时间 |
+| percall | cumtime的每次调用平均时间 |
+| filename:lineno(function) | 文件名, 行号，函数名 |
+
+
+### 寻找性能瓶颈
+
+通常`tottime`和`cumtime`是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。
+
+将性能分析结果按照tottime排序，效果如下:
+
+```text
+     4696   12.040    0.003   12.040    0.003 {built-in method run}
+   300005    0.874    0.000    1.681    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader)
+   107991    0.676    0.000    1.519    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:219(__init__)
+     4697    0.626    0.000    2.291    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)
+        1    0.618    0.618    0.618    0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/__init__.py:1(<module>)
+
+```
+
+可以看到最耗时的函数是C++端的`run`函数。这需要联合我们第二节`Python与C++混合代码的性能分析`来进行调优。而`sync_with_cpp`函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击`sync_with_cpp`的详细信息，了解其调用关系。
+
+```text
+Called By:
+
+   Ordered by: internal time
+   List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
+
+Function                                                                                                 was called by...
+                                                                                                             ncalls  tottime  cumtime
+/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)  <-    4697    0.626    2.291  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)
+/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)  <-    4696    0.019    2.316  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:487(clone)
+                                                                                                                  1    0.000    0.001  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:534(append_backward)
+
+
+Called:
+
+   Ordered by: internal time
+   List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
+```
+
+通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。
+
+
+
+## Python与C++混合代码的性能分析
+
+### 生成性能分析文件
+
+C++的性能分析工具非常多。常见的包括`gprof`, `valgrind`, `google-perftools`。但是调试Python中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而Python的一个第三方库`yep`提供了方便的和`google-perftools`交互的方法。于是这里使用`yep`进行Python与C++混合代码的性能分析
+
+使用`yep`前需要安装`google-perftools`与`yep`包。ubuntu下安装命令为
+
+```bash
+apt install libgoogle-perftools-dev
+pip install yep
+```
+
+安装完毕后，我们可以通过
+
+```bash
+python -m yep -v main.py
+```
+
+生成性能分析文件。生成的性能分析文件为`main.py.prof`。
+
+命令行中的`-v`指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为C++与Python不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施:
+
+1. 编译时指定`-g`生成调试信息。使用cmake的话，可以将CMAKE_BUILD_TYPE指定为`RelWithDebInfo`。
+2. 编译时一定要开启优化。单纯的`Debug`编译性能会和`-O2`或者`-O3`有非常大的差别。`Debug`模式下的性能测试是没有意义的。
+3. 运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟如果单线程调试更容易。可以设置`OMP_NUM_THREADS=1`这个环境变量关闭openmp优化。
+
+### 查看性能分析文件
+
+在运行完性能分析后，会生成性能分析结果文件。我们可以使用[pprof](https://github.com/google/pprof)来显示性能分析结果。注意，这里使用了用`Go`语言重构后的`pprof`，因为这个工具具有web服务界面，且展示效果更好。
+
+安装`pprof`的命令和一般的`Go`程序是一样的，其命令如下:
+
+```bash
+go get github.com/google/pprof
+```
+
+进而我们可以使用如下命令开启一个HTTP服务:
+
+```bash
+pprof -http=0.0.0.0:3213 `which python`  ./main.py.prof
+```
+
+这行命令中，`-http`指开启HTTP服务。`which python`会产生当前Python二进制的完整路径，进而指定了Python可执行文件的路径。`./main.py.prof`输入了性能分析结果。
+
+访问对应的网址，我们可以查看性能分析的结果。结果如下图所示:
+
+![result](./pprof_1.png)
+
+
+### 寻找性能瓶颈
+
+与寻找Python代码的性能瓶颈类似，寻找Python与C++混合代码的性能瓶颈也是要看`tottime`和`cumtime`。而`pprof`展示的调用图也可以帮助我们发现性能中的问题。
+
+例如下图中，
+
+![kernel_perf](./pprof_2.png)
+
+在一次训练中，乘法和乘法梯度的计算占用2%-4%左右的计算时间。而`MomentumOp`占用了17%左右的计算时间。显然，`MomentumOp`的性能有问题。
+
+在`pprof`中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。
+
+## 总结
+
+至此，两种性能分析的方式都介绍完毕了。希望通过这两种性能分析的方式，Paddle的开发人员和使用人员可以有次序的，科学的发现和解决性能问题。
diff --git a/develop/doc_cn/howto/optimization/cpu_profiling.html b/develop/doc_cn/howto/optimization/cpu_profiling.html
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index 00000000000..b1c4f2cc857
--- /dev/null
+++ b/develop/doc_cn/howto/optimization/cpu_profiling.html
@@ -0,0 +1,390 @@
+
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+  <ul class="wy-breadcrumbs">
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+    <li>Python代码的性能分析</li>
+  </ul>
+</div>
+      
+      <div class="wy-nav-content" id="doc-content">
+        <div class="rst-content">
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  <p>此教程会介绍如何使用Python的cProfile包，与Python库yep，google perftools来运行性能分析(Profiling)与调优。</p>
+<p>运行性能分析可以让开发人员科学的，有条不紊的对程序进行性能优化。性能分析是性能调优的基础。因为在程序实际运行中，真正的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。</p>
+<p>性能优化的步骤，通常是循环重复若干次『性能分析 &#8211;&gt; 寻找瓶颈 &#8212;&gt; 调优瓶颈 &#8211;&gt; 性能分析确认调优效果』。其中性能分析是性能调优的至关重要的量化指标。</p>
+<p>Paddle提供了Python语言绑定。用户使用Python进行神经网络编程，训练，测试。Python解释器通过<code class="docutils literal"><span class="pre">pybind</span></code>和<code class="docutils literal"><span class="pre">swig</span></code>调用Paddle的动态链接库，进而调用Paddle C++部分的代码。所以Paddle的性能分析与调优分为两个部分:</p>
+<ul class="simple">
+<li>Python代码的性能分析</li>
+<li>Python与C++混合代码的性能分析</li>
+</ul>
+<div class="section" id="python">
+<span id="python"></span><h1>Python代码的性能分析<a class="headerlink" href="#python" title="永久链接至标题">¶</a></h1>
+<div class="section" id="">
+<span id="id1"></span><h2>生成性能分析文件<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
+<p>Python标准库中提供了性能分析的工具包，<a class="reference external" href="https://docs.python.org/2/library/profile.html">cProfile</a>。生成Python性能分析的命令如下:</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>python -m cProfile -o profile.out main.py
+</pre></div>
+</div>
+<p>其中<code class="docutils literal"><span class="pre">-o</span></code>标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，<code class="docutils literal"><span class="pre">cProfile</span></code>会打印一些统计信息到<code class="docutils literal"><span class="pre">stdout</span></code>。这不方便我们进行后期处理(进行<code class="docutils literal"><span class="pre">sort</span></code>, <code class="docutils literal"><span class="pre">split</span></code>, <code class="docutils literal"><span class="pre">cut</span></code>等等)。</p>
+</div>
+<div class="section" id="">
+<span id="id2"></span><h2>查看性能分析文件<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
+<p>当main.py运行完毕后，性能分析结果文件<code class="docutils literal"><span class="pre">profile.out</span></code>就生成出来了。我们可以使用<a class="reference external" href="https://github.com/ymichael/cprofilev">cprofilev</a>来查看性能分析结果。<code class="docutils literal"><span class="pre">cprofilev</span></code>是一个Python的第三方库。使用它会开启一个HTTP服务，将性能分析结果以网页的形式展示出来。</p>
+<p>使用<code class="docutils literal"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">cprofilev</span></code>安装<code class="docutils literal"><span class="pre">cprofilev</span></code>工具。安装完成后，使用如下命令开启HTTP服务</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>cprofilev -a <span class="m">0</span>.0.0.0 -p <span class="m">3214</span> -f profile.out main.py
+</pre></div>
+</div>
+<p>其中<code class="docutils literal"><span class="pre">-a</span></code>标识HTTP服务绑定的IP。使用<code class="docutils literal"><span class="pre">0.0.0.0</span></code>允许外网访问这个HTTP服务。<code class="docutils literal"><span class="pre">-p</span></code>标识HTTP服务的端口。<code class="docutils literal"><span class="pre">-f</span></code>标识性能分析的结果文件。<code class="docutils literal"><span class="pre">main.py</span></code>标识被性能分析的源文件。</p>
+<p>访问对应网址，即可显示性能分析的结果。性能分析结果格式如下:</p>
+<div class="highlight-text"><div class="highlight"><pre><span></span>   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
+        1    0.284    0.284   29.514   29.514 main.py:1(&lt;module&gt;)
+     4696    0.128    0.000   15.748    0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/executor.py:20(run)
+     4696   12.040    0.003   12.040    0.003 {built-in method run}
+        1    0.144    0.144    6.534    6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14(&lt;module&gt;)
+</pre></div>
+</div>
+<p>每一列的含义是:</p>
+<p>| 列名 | 含义 |
+| &#8212; | &#8212; |
+| ncalls | 函数的调用次数 |
+| tottime | 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间 |
+| percall | tottime的每次调用平均时间 |
+| cumtime | 函数总时间。包含这个函数调用其他函数的时间 |
+| percall | cumtime的每次调用平均时间 |
+| filename:lineno(function) | 文件名, 行号，函数名 |</p>
+</div>
+<div class="section" id="">
+<span id="id3"></span><h2>寻找性能瓶颈<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
+<p>通常<code class="docutils literal"><span class="pre">tottime</span></code>和<code class="docutils literal"><span class="pre">cumtime</span></code>是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。</p>
+<p>将性能分析结果按照tottime排序，效果如下:</p>
+<div class="highlight-text"><div class="highlight"><pre><span></span>     4696   12.040    0.003   12.040    0.003 {built-in method run}
+   300005    0.874    0.000    1.681    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader)
+   107991    0.676    0.000    1.519    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:219(__init__)
+     4697    0.626    0.000    2.291    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)
+        1    0.618    0.618    0.618    0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/__init__.py:1(&lt;module&gt;)
+</pre></div>
+</div>
+<p>可以看到最耗时的函数是C++端的<code class="docutils literal"><span class="pre">run</span></code>函数。这需要联合我们第二节<code class="docutils literal"><span class="pre">Python与C++混合代码的性能分析</span></code>来进行调优。而<code class="docutils literal"><span class="pre">sync_with_cpp</span></code>函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击<code class="docutils literal"><span class="pre">sync_with_cpp</span></code>的详细信息，了解其调用关系。</p>
+<div class="highlight-text"><div class="highlight"><pre><span></span>Called By:
+
+   Ordered by: internal time
+   List reduced from 4497 to 2 due to restriction &lt;&#39;sync_with_cpp&#39;&gt;
+
+Function                                                                                                 was called by...
+                                                                                                             ncalls  tottime  cumtime
+/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)  &lt;-    4697    0.626    2.291  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)
+/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)  &lt;-    4696    0.019    2.316  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:487(clone)
+                                                                                                                  1    0.000    0.001  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:534(append_backward)
+
+
+Called:
+
+   Ordered by: internal time
+   List reduced from 4497 to 2 due to restriction &lt;&#39;sync_with_cpp&#39;&gt;
+</pre></div>
+</div>
+<p>通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。</p>
+</div>
+</div>
+<div class="section" id="pythonc">
+<span id="pythonc"></span><h1>Python与C++混合代码的性能分析<a class="headerlink" href="#pythonc" title="永久链接至标题">¶</a></h1>
+<div class="section" id="">
+<span id="id4"></span><h2>生成性能分析文件<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
+<p>C++的性能分析工具非常多。常见的包括<code class="docutils literal"><span class="pre">gprof</span></code>, <code class="docutils literal"><span class="pre">valgrind</span></code>, <code class="docutils literal"><span class="pre">google-perftools</span></code>。但是调试Python中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而Python的一个第三方库<code class="docutils literal"><span class="pre">yep</span></code>提供了方便的和<code class="docutils literal"><span class="pre">google-perftools</span></code>交互的方法。于是这里使用<code class="docutils literal"><span class="pre">yep</span></code>进行Python与C++混合代码的性能分析</p>
+<p>使用<code class="docutils literal"><span class="pre">yep</span></code>前需要安装<code class="docutils literal"><span class="pre">google-perftools</span></code>与<code class="docutils literal"><span class="pre">yep</span></code>包。ubuntu下安装命令为</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>apt install libgoogle-perftools-dev
+pip install yep
+</pre></div>
+</div>
+<p>安装完毕后，我们可以通过</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>python -m yep -v main.py
+</pre></div>
+</div>
+<p>生成性能分析文件。生成的性能分析文件为<code class="docutils literal"><span class="pre">main.py.prof</span></code>。</p>
+<p>命令行中的<code class="docutils literal"><span class="pre">-v</span></code>指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为C++与Python不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施:</p>
+<ol class="simple">
+<li>编译时指定<code class="docutils literal"><span class="pre">-g</span></code>生成调试信息。使用cmake的话，可以将CMAKE_BUILD_TYPE指定为<code class="docutils literal"><span class="pre">RelWithDebInfo</span></code>。</li>
+<li>编译时一定要开启优化。单纯的<code class="docutils literal"><span class="pre">Debug</span></code>编译性能会和<code class="docutils literal"><span class="pre">-O2</span></code>或者<code class="docutils literal"><span class="pre">-O3</span></code>有非常大的差别。<code class="docutils literal"><span class="pre">Debug</span></code>模式下的性能测试是没有意义的。</li>
+<li>运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟如果单线程调试更容易。可以设置<code class="docutils literal"><span class="pre">OMP_NUM_THREADS=1</span></code>这个环境变量关闭openmp优化。</li>
+</ol>
+</div>
+<div class="section" id="">
+<span id="id5"></span><h2>查看性能分析文件<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
+<p>在运行完性能分析后，会生成性能分析结果文件。我们可以使用<a class="reference external" href="https://github.com/google/pprof">pprof</a>来显示性能分析结果。注意，这里使用了用<code class="docutils literal"><span class="pre">Go</span></code>语言重构后的<code class="docutils literal"><span class="pre">pprof</span></code>，因为这个工具具有web服务界面，且展示效果更好。</p>
+<p>安装<code class="docutils literal"><span class="pre">pprof</span></code>的命令和一般的<code class="docutils literal"><span class="pre">Go</span></code>程序是一样的，其命令如下:</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>go get github.com/google/pprof
+</pre></div>
+</div>
+<p>进而我们可以使用如下命令开启一个HTTP服务:</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>pprof -http<span class="o">=</span><span class="m">0</span>.0.0.0:3213 <span class="sb">`</span>which python<span class="sb">`</span>  ./main.py.prof
+</pre></div>
+</div>
+<p>这行命令中，<code class="docutils literal"><span class="pre">-http</span></code>指开启HTTP服务。<code class="docutils literal"><span class="pre">which</span> <span class="pre">python</span></code>会产生当前Python二进制的完整路径，进而指定了Python可执行文件的路径。<code class="docutils literal"><span class="pre">./main.py.prof</span></code>输入了性能分析结果。</p>
+<p>访问对应的网址，我们可以查看性能分析的结果。结果如下图所示:</p>
+<p><img alt="result" src="../../_images/pprof_1.png" /></p>
+</div>
+<div class="section" id="">
+<span id="id6"></span><h2>寻找性能瓶颈<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
+<p>与寻找Python代码的性能瓶颈类似，寻找Python与C++混合代码的性能瓶颈也是要看<code class="docutils literal"><span class="pre">tottime</span></code>和<code class="docutils literal"><span class="pre">cumtime</span></code>。而<code class="docutils literal"><span class="pre">pprof</span></code>展示的调用图也可以帮助我们发现性能中的问题。</p>
+<p>例如下图中，</p>
+<p><img alt="kernel_perf" src="../../_images/pprof_2.png" /></p>
+<p>在一次训练中，乘法和乘法梯度的计算占用2%-4%左右的计算时间。而<code class="docutils literal"><span class="pre">MomentumOp</span></code>占用了17%左右的计算时间。显然，<code class="docutils literal"><span class="pre">MomentumOp</span></code>的性能有问题。</p>
+<p>在<code class="docutils literal"><span class="pre">pprof</span></code>中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。</p>
+</div>
+</div>
+<div class="section" id="">
+<span id="id7"></span><h1>总结<a class="headerlink" href="#" title="永久链接至标题">¶</a></h1>
+<p>至此，两种性能分析的方式都介绍完毕了。希望通过这两种性能分析的方式，Paddle的开发人员和使用人员可以有次序的，科学的发现和解决性能问题。</p>
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diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js
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