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develop/doc/_sources/design/refactor/multi_cpu.md.txt 0 → 100644
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# Design Doc: Execute the Program with Multi CPU
## Abstract
This Design Doc propose an approach to make the user-defined Op graph
running with multi-CPU, we will use an auto transpiler to convert the user-defined
Op graph to a multi-CPU Op graph, and run `ParallelDo` Op to run the graph.
## Transpiler
<img src="src/multi-threads/single-thread@3x.png" width="300">
After converted:
<img src="src/multi-threads/multi-threads@3x.png" width="1000">
## Implement
- `Multi-CPU Transpiler` will convert the graph to a multi-CPU graph
which would be executed with multi-threads.
- `BlockingCounter` will `Init/Decrement` an atomic counter, and Blocking `Wait`
for the atomic counter become `0`:
```cpp
BlockingCounter bc(thread_count);
for (int i = 0; i < thread_count; ++i) {
thread_pool->Start([&bc] {bc.DecrementCount(); })
}
bc.Wait();
```
- `ParallelDo` Operator
- Initialize a thread pool which is a Singleton.
- Use a block id as the input, and create run the specify Block on independent scope
with multi-threads.
- Initialize a `BlockingCounter` instance and wait until all threads are done.
- `Split` Operator will split the Input Tensor into a TensorArray.
- `Merge` merge all the gradients which calculated in different threads
with `mean/sum/max/min...` method, and then run the Optimizer Op to optimize `W`.
## TODO
- Improve the optimizer stage with multi-threads, since we could
assign the parameters to the different threads and execute
optimizer with multi-threads.
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<li>Design Doc: Execute the Program with Multi CPU</li>
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<div class="section" id="design-doc-execute-the-program-with-multi-cpu">
<span id="design-doc-execute-the-program-with-multi-cpu"></span><h1>Design Doc: Execute the Program with Multi CPU<a class="headerlink" href="#design-doc-execute-the-program-with-multi-cpu" title="Permalink to this headline"></a></h1>
<div class="section" id="abstract">
<span id="abstract"></span><h2>Abstract<a class="headerlink" href="#abstract" title="Permalink to this headline"></a></h2>
<p>This Design Doc propose an approach to make the user-defined Op graph
running with multi-CPU, we will use an auto transpiler to convert the user-defined
Op graph to a multi-CPU Op graph, and run <code class="docutils literal"><span class="pre">ParallelDo</span></code> Op to run the graph.</p>
</div>
<div class="section" id="transpiler">
<span id="transpiler"></span><h2>Transpiler<a class="headerlink" href="#transpiler" title="Permalink to this headline"></a></h2>
<p><img src="src/multi-threads/single-thread@3x.png" width="300"></p>
<p>After converted:</p>
<p><img src="src/multi-threads/multi-threads@3x.png" width="1000"></p>
</div>
<div class="section" id="implement">
<span id="implement"></span><h2>Implement<a class="headerlink" href="#implement" title="Permalink to this headline"></a></h2>
<ul>
<li><p class="first"><code class="docutils literal"><span class="pre">Multi-CPU</span> <span class="pre">Transpiler</span></code> will convert the graph to a multi-CPU graph
which would be executed with multi-threads.</p>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">BlockingCounter</span></code> will <code class="docutils literal"><span class="pre">Init/Decrement</span></code> an atomic counter, and Blocking <code class="docutils literal"><span class="pre">Wait</span></code>
for the atomic counter become <code class="docutils literal"><span class="pre">0</span></code>:</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="n">BlockingCounter</span> <span class="nf">bc</span><span class="p">(</span><span class="n">thread_count</span><span class="p">);</span>
<span class="k">for</span> <span class="p">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">thread_count</span><span class="p">;</span> <span class="o">++</span><span class="n">i</span><span class="p">)</span> <span class="p">{</span>
<span class="n">thread_pool</span><span class="o">-&gt;</span><span class="n">Start</span><span class="p">([</span><span class="o">&amp;</span><span class="n">bc</span><span class="p">]</span> <span class="p">{</span><span class="n">bc</span><span class="p">.</span><span class="n">DecrementCount</span><span class="p">();</span> <span class="p">})</span>
<span class="p">}</span>
<span class="n">bc</span><span class="p">.</span><span class="n">Wait</span><span class="p">();</span>
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">ParallelDo</span></code> Operator</p>
<ul class="simple">
<li>Initialize a thread pool which is a Singleton.</li>
<li>Use a block id as the input, and create run the specify Block on independent scope
with multi-threads.</li>
<li>Initialize a <code class="docutils literal"><span class="pre">BlockingCounter</span></code> instance and wait until all threads are done.</li>
</ul>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">Split</span></code> Operator will split the Input Tensor into a TensorArray.</p>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">Merge</span></code> merge all the gradients which calculated in different threads
with <code class="docutils literal"><span class="pre">mean/sum/max/min...</span></code> method, and then run the Optimizer Op to optimize <code class="docutils literal"><span class="pre">W</span></code>.</p>
</li>
</ul>
</div>
<div class="section" id="todo">
<span id="todo"></span><h2>TODO<a class="headerlink" href="#todo" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li>Improve the optimizer stage with multi-threads, since we could
assign the parameters to the different threads and execute
optimizer with multi-threads.</li>
</ul>
</div>
</div>
</div>
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develop/doc_cn/_sources/design/refactor/multi_cpu.md.txt 0 → 100644
浏览文件 @ 89ca4b91
# Design Doc: Execute the Program with Multi CPU
## Abstract
This Design Doc propose an approach to make the user-defined Op graph
running with multi-CPU, we will use an auto transpiler to convert the user-defined
Op graph to a multi-CPU Op graph, and run `ParallelDo` Op to run the graph.
## Transpiler
<img src="src/multi-threads/single-thread@3x.png" width="300">
After converted:
<img src="src/multi-threads/multi-threads@3x.png" width="1000">
## Implement
- `Multi-CPU Transpiler` will convert the graph to a multi-CPU graph
which would be executed with multi-threads.
- `BlockingCounter` will `Init/Decrement` an atomic counter, and Blocking `Wait`
for the atomic counter become `0`:
```cpp
BlockingCounter bc(thread_count);
for (int i = 0; i < thread_count; ++i) {
thread_pool->Start([&bc] {bc.DecrementCount(); })
}
bc.Wait();
```
- `ParallelDo` Operator
- Initialize a thread pool which is a Singleton.
- Use a block id as the input, and create run the specify Block on independent scope
with multi-threads.
- Initialize a `BlockingCounter` instance and wait until all threads are done.
- `Split` Operator will split the Input Tensor into a TensorArray.
- `Merge` merge all the gradients which calculated in different threads
with `mean/sum/max/min...` method, and then run the Optimizer Op to optimize `W`.
## TODO
- Improve the optimizer stage with multi-threads, since we could
assign the parameters to the different threads and execute
optimizer with multi-threads.
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<li>Design Doc: Execute the Program with Multi CPU</li>
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<span id="design-doc-execute-the-program-with-multi-cpu"></span><h1>Design Doc: Execute the Program with Multi CPU<a class="headerlink" href="#design-doc-execute-the-program-with-multi-cpu" title="永久链接至标题"></a></h1>
<div class="section" id="abstract">
<span id="abstract"></span><h2>Abstract<a class="headerlink" href="#abstract" title="永久链接至标题"></a></h2>
<p>This Design Doc propose an approach to make the user-defined Op graph
running with multi-CPU, we will use an auto transpiler to convert the user-defined
Op graph to a multi-CPU Op graph, and run <code class="docutils literal"><span class="pre">ParallelDo</span></code> Op to run the graph.</p>
</div>
<div class="section" id="transpiler">
<span id="transpiler"></span><h2>Transpiler<a class="headerlink" href="#transpiler" title="永久链接至标题"></a></h2>
<p><img src="src/multi-threads/single-thread@3x.png" width="300"></p>
<p>After converted:</p>
<p><img src="src/multi-threads/multi-threads@3x.png" width="1000"></p>
</div>
<div class="section" id="implement">
<span id="implement"></span><h2>Implement<a class="headerlink" href="#implement" title="永久链接至标题"></a></h2>
<ul>
<li><p class="first"><code class="docutils literal"><span class="pre">Multi-CPU</span> <span class="pre">Transpiler</span></code> will convert the graph to a multi-CPU graph
which would be executed with multi-threads.</p>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">BlockingCounter</span></code> will <code class="docutils literal"><span class="pre">Init/Decrement</span></code> an atomic counter, and Blocking <code class="docutils literal"><span class="pre">Wait</span></code>
for the atomic counter become <code class="docutils literal"><span class="pre">0</span></code>:</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="n">BlockingCounter</span> <span class="nf">bc</span><span class="p">(</span><span class="n">thread_count</span><span class="p">);</span>
<span class="k">for</span> <span class="p">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">thread_count</span><span class="p">;</span> <span class="o">++</span><span class="n">i</span><span class="p">)</span> <span class="p">{</span>
<span class="n">thread_pool</span><span class="o">-&gt;</span><span class="n">Start</span><span class="p">([</span><span class="o">&amp;</span><span class="n">bc</span><span class="p">]</span> <span class="p">{</span><span class="n">bc</span><span class="p">.</span><span class="n">DecrementCount</span><span class="p">();</span> <span class="p">})</span>
<span class="p">}</span>
<span class="n">bc</span><span class="p">.</span><span class="n">Wait</span><span class="p">();</span>
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">ParallelDo</span></code> Operator</p>
<ul class="simple">
<li>Initialize a thread pool which is a Singleton.</li>
<li>Use a block id as the input, and create run the specify Block on independent scope
with multi-threads.</li>
<li>Initialize a <code class="docutils literal"><span class="pre">BlockingCounter</span></code> instance and wait until all threads are done.</li>
</ul>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">Split</span></code> Operator will split the Input Tensor into a TensorArray.</p>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">Merge</span></code> merge all the gradients which calculated in different threads
with <code class="docutils literal"><span class="pre">mean/sum/max/min...</span></code> method, and then run the Optimizer Op to optimize <code class="docutils literal"><span class="pre">W</span></code>.</p>
</li>
</ul>
</div>
<div class="section" id="todo">
<span id="todo"></span><h2>TODO<a class="headerlink" href="#todo" title="永久链接至标题"></a></h2>
<ul class="simple">
<li>Improve the optimizer stage with multi-threads, since we could
assign the parameters to the different threads and execute
optimizer with multi-threads.</li>
</ul>
</div>
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