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  <span class="target" id="module-pyviennacl"></span><div class="section" id="pyviennacl">
<h1>PyViennaCL<a class="headerlink" href="#pyviennacl" title="Permalink to this headline"></a></h1>
<p>This extension provides the Python bindings for the ViennaCL linear
algebra and numerical computation library for GPGPU and heterogeneous
systems. ViennaCL itself is a header-only C++ library, so these bindings
make available to Python programmers ViennaCL&#8217;s fast OpenCL and CUDA 
algorithms, in a way that is idiomatic and compatible with the Python
community&#8217;s most popular scientific packages, NumPy and SciPy.</p>
<p>PyViennaCL is divided into five submodules, of which four are designed
for direct use by users:</p>
<blockquote>
<div><ul class="simple">
<li><a class="reference internal" href="pycore.html"><em>pycore</em></a>: user-friendly classes for representing the main ViennaCL
objects, such as Matrix or Vector;</li>
<li><a class="reference internal" href="linalg.html"><em>linalg</em></a>: an explicit interface to a number of ViennaCL&#8217;s linear
algebra routines, such as matrix solvers and eigenvalue computation;</li>
<li><a class="reference internal" href="vclmath.html"><em>vclmath</em></a>: convenience functions akin to the standard math module
(you can also access it as <code class="docutils literal"><span class="pre">pyviennacl.math</span></code>);</li>
<li><a class="reference internal" href="util.html"><em>util</em></a>: utility functions, such as to construct an appropriate
ViennaCL object from an ndarray (Matrix or Vector), or to provide basic
debug logging;</li>
<li>_viennacl: a raw C++ interface to ViennaCL, with no stable API.</li>
</ul>
</div></blockquote>
<p>Nonetheless, all of PyViennaCL&#8217;s functionality is available from the
top-level pyviennacl namespace. So, if you want help on the Matrix class,
you can just run:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pyviennacl</span> <span class="kn">as</span> <span class="nn">p</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">help</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">Matrix</span><span class="p">)</span>                                     
</pre></div>
</div>
<p>For help on PyViennaCL&#8217;s core functionality in general, or
PyViennaCL&#8217;s high-level linear algebra functions, run:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="n">help</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">pycore</span><span class="p">)</span>                                    
</pre></div>
</div>
<p>or:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="n">help</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">linalg</span><span class="p">)</span>                                    
</pre></div>
</div>
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<div class="section" id="usage-examples">
<h1>Usage examples<a class="headerlink" href="#usage-examples" title="Permalink to this headline"></a></h1>
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<li class="toctree-l2"><a class="reference internal" href="examples/basics.html">PyViennaCL Basics</a></li>
<li class="toctree-l2"><a class="reference internal" href="examples/products.html">Computing products</a></li>
<li class="toctree-l2"><a class="reference internal" href="examples/slices-and-proxies.html">Submatrices: slices and proxies</a></li>
<li class="toctree-l2"><a class="reference internal" href="examples/sparse-matrices.html">Sparse matrices</a></li>
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<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline"></a></h1>
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<li><a class="reference internal" href="#">PyViennaCL</a></li>
<li><a class="reference internal" href="#usage-examples">Usage examples</a></li>
<li><a class="reference internal" href="#indices-and-tables">Indices and tables</a></li>
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