[GH-PAGES] Updated website
This commit is contained in:
Philippe Tillet
@@ -323,7 +323,7 @@ for different problem sizes.</p>
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15 134217728.0 851.577704 850.656574
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</pre></div>
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</div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 11.032 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 10.984 seconds)</p>
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-01-vector-add-py">
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
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<p><a class="reference download internal" download="" href="../../_downloads/62d97d49a32414049819dd8bb8378080/01-vector-add.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">01-vector-add.py</span></code></a></p>
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@@ -216,7 +216,7 @@ Let us consider instead the case of a simple (numerically stabilized) softmax op
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<span class="c1"># read 2MN elements ; write MN elements</span>
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<span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="n">x_max</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span>
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<span class="c1"># read MN elements ; write MN elements</span>
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<span class="n">numerator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<span class="n">numerator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
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<span class="c1"># read MN elements ; write M elements</span>
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<span class="n">denominator</span> <span class="o">=</span> <span class="n">numerator</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<span class="c1"># read 2MN elements ; write MN elements</span>
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@@ -346,17 +346,17 @@ We will then compare its performance against (1) <code class="code docutils lite
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>softmax-performance:
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N Triton Torch (native) Torch (jit)
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0 256.0 512.000001 546.133347 273.066674
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1 384.0 585.142862 585.142862 267.130429
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2 512.0 630.153853 606.814814 264.258068
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3 640.0 682.666684 640.000002 269.473696
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4 768.0 702.171410 664.216187 273.066663
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0 256.0 512.000001 546.133347 186.181817
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1 384.0 585.142862 585.142862 153.600004
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2 512.0 630.153853 606.814814 154.566038
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3 640.0 682.666684 640.000002 160.000000
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4 768.0 702.171410 664.216187 162.754967
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.. ... ... ... ...
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93 12160.0 812.359066 406.179533 329.483481
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94 12288.0 812.429770 415.661740 329.602681
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95 12416.0 810.840807 412.149375 329.173158
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96 12544.0 810.925276 412.546756 329.292871
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97 12672.0 811.007961 412.097543 329.410251
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93 12160.0 812.359066 406.179533 198.936606
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94 12288.0 812.429770 416.101597 199.298541
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95 12416.0 810.840807 412.149375 198.854847
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96 12544.0 810.925276 412.971190 199.111113
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97 12672.0 811.007961 412.097543 199.167004
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[98 rows x 4 columns]
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</pre></div>
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@@ -370,7 +370,7 @@ This means that – when temporary data is too large to fit entirely in the GPU
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Note that our Triton kernel is not only faster than PyTorch’s CUDA kernel, it is also <strong>easier to read, understand and maintain</strong>.</p></li>
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</ul>
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</div></blockquote>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 8.174 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 13.269 seconds)</p>
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-02-fused-softmax-py">
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
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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>
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@@ -476,42 +476,42 @@ tensor(True, device='cuda:0')
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>matmul-performance:
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M cuBLAS ... Triton Triton (+ LeakyReLU)
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0 128.0 0.455111 ... 0.512000 0.512000
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1 256.0 2.978909 ... 2.978909 2.978909
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2 384.0 7.372800 ... 7.899428 7.899428
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1 256.0 2.730667 ... 2.978909 2.978909
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2 384.0 7.372800 ... 8.507077 8.507077
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3 512.0 14.563555 ... 16.384000 15.420235
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4 640.0 22.260869 ... 24.380953 24.380953
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5 768.0 32.768000 ... 34.028308 34.028308
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6 896.0 39.025776 ... 39.025776 39.025776
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7 1024.0 51.150050 ... 52.428801 52.428801
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7 1024.0 49.932191 ... 52.428801 52.428801
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8 1152.0 44.566925 ... 46.656000 46.656000
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9 1280.0 51.200001 ... 56.109587 56.109587
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10 1408.0 64.138541 ... 65.684049 65.684049
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11 1536.0 80.430545 ... 76.106321 75.296679
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12 1664.0 63.372618 ... 62.061463 61.636381
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13 1792.0 72.983276 ... 68.953520 68.533074
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14 1920.0 69.120002 ... 68.435645 68.435645
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15 2048.0 73.908442 ... 75.573044 75.234154
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16 2176.0 83.500614 ... 80.173899 79.855747
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17 2304.0 68.446623 ... 73.051599 72.607513
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18 2432.0 71.125224 ... 81.197876 80.963875
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19 2560.0 77.649287 ... 76.027843 76.740048
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20 2688.0 83.552988 ... 83.186525 82.823267
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21 2816.0 84.035084 ... 76.921000 79.733474
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22 2944.0 82.102191 ... 80.122235 78.729910
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23 3072.0 82.540970 ... 82.661468 82.661468
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24 3200.0 84.432717 ... 89.385477 84.432717
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25 3328.0 83.905938 ... 86.113988 86.528001
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26 3456.0 82.015834 ... 83.545665 84.156124
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27 3584.0 87.466332 ... 92.600816 84.988707
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28 3712.0 85.163978 ... 82.902362 83.666116
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29 3840.0 84.292684 ... 84.550462 85.070769
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30 3968.0 89.921841 ... 87.472354 87.409694
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31 4096.0 93.792965 ... 89.478485 90.260743
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10 1408.0 64.138541 ... 65.684049 64.902096
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11 1536.0 80.430545 ... 76.106321 76.106321
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12 1664.0 63.372618 ... 61.636381 61.636381
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13 1792.0 72.983276 ... 69.379162 68.953520
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14 1920.0 68.435645 ... 69.818184 68.776119
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15 2048.0 73.584279 ... 75.915006 75.234154
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16 2176.0 83.500614 ... 78.608000 79.540109
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17 2304.0 68.251065 ... 73.051599 72.607513
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18 2432.0 71.125224 ... 81.669953 80.269900
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19 2560.0 77.833728 ... 75.328737 74.812787
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20 2688.0 81.576466 ... 81.227100 82.823267
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21 2816.0 83.392363 ... 78.868366 77.330158
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22 2944.0 81.698415 ... 80.122235 79.356738
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23 3072.0 80.890151 ... 81.589488 83.391907
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24 3200.0 84.656085 ... 85.447263 88.033012
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25 3328.0 82.939284 ... 86.632127 82.558825
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26 3456.0 81.518272 ... 84.954233 84.420490
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27 3584.0 83.254996 ... 94.747514 95.654673
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28 3712.0 86.192706 ... 88.955779 88.797643
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29 3840.0 84.809814 ... 84.874902 84.356981
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30 3968.0 89.593789 ... 84.738843 84.212518
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31 4096.0 93.596744 ... 83.833686 90.933423
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[32 rows x 5 columns]
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</pre></div>
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</div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 2.376 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 16.432 seconds)</p>
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-03-matrix-multiplication-py">
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
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<p><a class="reference download internal" download="" href="../../_downloads/d5fee5b55a64e47f1b5724ec39adf171/03-matrix-multiplication.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">03-matrix-multiplication.py</span></code></a></p>
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@@ -174,7 +174,7 @@
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<div class="section" id="computation-times">
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<span id="sphx-glr-getting-started-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
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<p><strong>03:21.583</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
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<p><strong>03:40.685</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
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<table class="docutils align-default">
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<colgroup>
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<col style="width: 85%" />
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@@ -183,15 +183,15 @@
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</colgroup>
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<tbody>
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<tr class="row-odd"><td><p><a class="reference internal" href="03-matrix-multiplication.html#sphx-glr-getting-started-tutorials-03-matrix-multiplication-py"><span class="std std-ref">Matrix Multiplication</span></a> (<code class="docutils literal notranslate"><span class="pre">03-matrix-multiplication.py</span></code>)</p></td>
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<td><p>02:02.376</p></td>
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<td><p>02:16.432</p></td>
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<td><p>0.0 MB</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="02-fused-softmax.html#sphx-glr-getting-started-tutorials-02-fused-softmax-py"><span class="std std-ref">Fused Softmax</span></a> (<code class="docutils literal notranslate"><span class="pre">02-fused-softmax.py</span></code>)</p></td>
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<td><p>01:08.174</p></td>
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<td><p>01:13.269</p></td>
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<td><p>0.0 MB</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="01-vector-add.html#sphx-glr-getting-started-tutorials-01-vector-add-py"><span class="std std-ref">Vector Addition</span></a> (<code class="docutils literal notranslate"><span class="pre">01-vector-add.py</span></code>)</p></td>
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<td><p>00:11.032</p></td>
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<td><p>00:10.984</p></td>
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<td><p>0.0 MB</p></td>
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</tr>
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</tbody>
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