[GH-PAGES] Updated website

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Philippe Tillet
2021-03-23 17:10:07 -04:00
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commit 64141f0fca
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@@ -109,9 +109,10 @@
</ul>
<p class="caption"><span class="caption-text">Programming Guide</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/introduction.html">Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/related-work.html">Related Work</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/triton-c.html">The Triton-C Language</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/chapter-1/introduction.html">Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/chapter-2/related-work.html">Related Work</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/chapter-3/triton-c.html">The Triton-C Language</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/chapter-4/triton-ir.html">The Triton-IR Intermediate Representation</a></li>
</ul>
@@ -404,7 +405,7 @@ This means that when temporary data is too large to fit entirely in the GPU
Note that our Triton kernel is not only faster than PyTorchs CUDA kernel, it is also <strong>easier to read, understand and maintain</strong>.</p></li>
</ul>
</div></blockquote>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 19.933 seconds)</p>
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<p><a class="reference download internal" download="" href="../../_downloads/d91442ac2982c4e0cc3ab0f43534afbc/02-fused-softmax.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">02-fused-softmax.py</span></code></a></p>