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<div class="section" id="pyviennacl-basics">
<h1>PyViennaCL Basics<a class="headerlink" href="#pyviennacl-basics" title="Permalink to this headline">ΒΆ</a></h1>
<div class="highlight-python"><div class="highlight"><pre><span class="c">#!python</span>
<span class="sd">"""</span>
<span class="sd">In this example, we investigate the construction and basic usage of</span>
<span class="sd">PyViennaCL's dense matrix (Matrix) and Vector types, and discuss some</span>
<span class="sd">important issues about integration with Python and NumPy data types, and</span>
<span class="sd">PyViennaCL's computational architecture.</span>
<span class="sd">If you are familiar with NumPy, you might need about 5 minutes to grasp the</span>
<span class="sd">content here. If you are not so familiar, you might need 10 minutes.</span>
<span class="sd">"""</span>
<span class="c"># Import PyViennaCL and NumPy</span>
<span class="kn">import</span> <span class="nn">pyviennacl</span> <span class="kn">as</span> <span class="nn">p</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="c"># Create our datastructures on the host</span>
<span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">]</span> <span class="c"># We can create PyViennaCL Vectors from lists</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">],</span>
<span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span>
<span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">]])</span> <span class="c"># We can create PyViennaCL Matrices from arrays</span>
<span class="c"># Create corresponding ViennaCL datastructures on the compute device</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">Vector</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">Matrix</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="c"># This is a dense matrix</span>
<span class="c"># Copy the data back to the host and check that it's equal</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">value</span> <span class="c"># z is now a 1-D numpy array with dtype float64</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">value</span> <span class="c"># c is now a 2-D numpy array with dtype float64</span>
<span class="k">if</span> <span class="p">(</span><span class="n">z</span> <span class="o">==</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span> <span class="ow">and</span> <span class="p">(</span><span class="n">c</span> <span class="o">==</span> <span class="n">a</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Successfully transferred data to and from the compute device!"</span><span class="p">)</span>
<span class="c"># We can modify elements of the ViennaCL device structures, but since this </span>
<span class="c"># incurs a compute kernel initialisation and buffer transfer, it is very slow!</span>
<span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">b</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span>
<span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span> <span class="c"># We should also modify our original data to keep track..</span>
<span class="n">a</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span>
<span class="c"># And we can do comparisons seamlessly between NumPy and PyViennaCL types!</span>
<span class="k">if</span> <span class="p">(</span><span class="n">a</span> <span class="o">==</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span> <span class="ow">and</span> <span class="p">(</span><span class="n">x</span> <span class="o">==</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Successfully modified data structures on host and device!"</span><span class="p">)</span>
<span class="c"># We also need to be sure that we are consistent with respect to the data-types</span>
<span class="c"># we use. For instance, we should not mix integer and floating point types.</span>
<span class="c">#</span>
<span class="c"># By default, PyViennaCL objects inherit the dtype of the objects from which</span>
<span class="c"># they are created, or (if that is ambiguous), float64.</span>
<span class="k">print</span><span class="p">(</span><span class="s">"a and b have dtypes of </span><span class="si">%s</span><span class="s"> and </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span>
<span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">result_type</span><span class="p">(</span><span class="n">a</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">result_type</span><span class="p">(</span><span class="n">b</span><span class="p">)))</span>
<span class="c"># PyViennaCL makes an effort to convert objects to the appropriate dtype where</span>
<span class="c"># dtypes have been mixed, but this is often ambiguous and unpredictable, and</span>
<span class="c"># so it is recommended that users make efforts to keep they dtypes consistent.</span>
<span class="n">i</span> <span class="o">=</span> <span class="il">1L</span> <span class="c"># Create a long integer</span>
<span class="k">print</span><span class="p">(</span><span class="s">"i has dtype of </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">result_type</span><span class="p">(</span><span class="n">i</span><span class="p">)))</span>
<span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span> <span class="c"># Remember, this sort of elementwise assignation is *very slow*!</span>
<span class="k">print</span><span class="p">(</span><span class="s">"y has values </span><span class="si">%s</span><span class="s"> and dtype </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span>
<span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">result_type</span><span class="p">(</span><span class="n">y</span><span class="p">)))</span>
<span class="c"># And, of course, we can perform basic arithemetic operations with PyViennaCL,</span>
<span class="c"># mixing native Python types with NumPy and PyViennaCL types:</span>
<span class="n">z</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span> <span class="o">+</span> <span class="n">z</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span>
<span class="k">print</span><span class="p">(</span><span class="s">"z is now of type </span><span class="si">%s</span><span class="s">, dtype </span><span class="si">%s</span><span class="s">, and with values </span><span class="si">%s</span><span class="s">"</span>
<span class="o">%</span> <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">z</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">result_type</span><span class="p">(</span><span class="n">z</span><span class="p">),</span> <span class="n">z</span><span class="p">))</span>
<span class="c"># Notice that z has `Div' type. This is because the z object represents the</span>
<span class="c"># arithmetic expression `(x + y + z) / 2.0', and this is only computed when</span>
<span class="c"># the result is needed, in order to maximise performance.</span>
<span class="c"># And we can do less basic arithmetic!</span>
<span class="k">print</span><span class="p">(</span><span class="s">"The sine of the values of z is </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span>
<span class="n">p</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">z</span><span class="p">))</span>
<span class="c"># PyViennaCL exposes many elementwise mathematical functions.</span>
<span class="c"># See help(p.math) for more information. </span>
</pre></div>
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