[GH-PAGES] Updated website
This commit is contained in:
Philippe Tillet
@@ -320,25 +320,25 @@ for different problem sizes.</p>
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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>vector-add-performance:
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size Triton Torch
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0 4096.0 9.600000 9.600000
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0 4096.0 8.000000 9.600000
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1 8192.0 19.200000 19.200000
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2 16384.0 38.400001 38.400001
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3 32768.0 76.800002 76.800002
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4 65536.0 127.999995 127.999995
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5 131072.0 219.428568 219.428568
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6 262144.0 341.333321 384.000001
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6 262144.0 341.333321 341.333321
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7 524288.0 472.615390 472.615390
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8 1048576.0 614.400016 614.400016
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9 2097152.0 722.823517 722.823517
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9 2097152.0 702.171410 702.171410
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10 4194304.0 780.190482 780.190482
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11 8388608.0 812.429770 812.429770
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12 16777216.0 833.084721 833.084721
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13 33554432.0 843.811163 843.811163
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13 33554432.0 843.811163 842.004273
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14 67108864.0 848.362445 848.362445
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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> ( 1 minutes 9.393 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 10.996 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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@@ -388,15 +388,15 @@ We will then compare its performance against (1) <code class="code docutils lite
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N Triton Torch (native) Torch (jit)
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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 163.839992
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2 512.0 630.153853 585.142849 154.566038
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3 640.0 660.645170 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 405.755985 199.038365
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94 12288.0 812.429770 415.661740 199.298541
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95 12416.0 810.840807 412.149375 198.854847
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94 12288.0 812.429770 415.222812 199.298541
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95 12416.0 810.840807 411.722274 198.954424
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96 12544.0 810.925276 412.971190 199.111113
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97 12672.0 811.007961 412.516771 199.264875
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97 12672.0 811.007961 412.097543 199.264875
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[98 rows x 4 columns]
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</pre></div>
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@@ -409,7 +409,7 @@ We will then compare its performance against (1) <code class="code docutils lite
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Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</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 14.917 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 12.586 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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@@ -567,42 +567,42 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
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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>matmul-performance:
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M cuBLAS ... Triton Triton (+ LeakyReLU)
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0 256.0 2.978909 ... 2.978909 2.978909
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1 384.0 7.372800 ... 8.507077 7.899428
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2 512.0 14.563555 ... 16.384000 16.384000
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0 256.0 2.730667 ... 2.978909 2.978909
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1 384.0 7.372800 ... 7.899428 7.899428
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2 512.0 14.563555 ... 16.384000 15.420235
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3 640.0 22.260869 ... 24.380953 24.380953
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4 768.0 32.768000 ... 34.028308 34.028308
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5 896.0 39.025776 ... 40.140799 39.025776
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5 896.0 37.971025 ... 40.140799 39.025776
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6 1024.0 49.932191 ... 52.428801 52.428801
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7 1152.0 44.566925 ... 46.656000 45.938215
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7 1152.0 44.566925 ... 46.656000 46.656000
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8 1280.0 51.200001 ... 56.888887 56.109587
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9 1408.0 64.138541 ... 63.392744 62.664092
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10 1536.0 80.430545 ... 76.106321 76.106321
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10 1536.0 80.430545 ... 76.106321 75.296679
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11 1664.0 63.372618 ... 62.061463 62.061463
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12 1792.0 72.983276 ... 62.790080 62.441243
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13 1920.0 69.467336 ... 67.434145 70.172588
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14 2048.0 73.908442 ... 74.565406 74.565406
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15 2176.0 81.143743 ... 80.494588 80.817862
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16 2304.0 68.056616 ... 73.728002 73.501144
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17 2432.0 71.305746 ... 80.499895 80.269900
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18 2560.0 78.019048 ... 77.465723 77.283019
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19 2688.0 83.004501 ... 83.737433 84.483418
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20 2816.0 81.521884 ... 78.301990 80.767055
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21 2944.0 82.102191 ... 77.626218 71.194334
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22 3072.0 82.540970 ... 83.025078 83.269271
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23 3200.0 84.656085 ... 90.651555 84.993363
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24 3328.0 83.130825 ... 86.528001 87.262177
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25 3456.0 79.430113 ... 80.460651 83.459178
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26 3584.0 87.466332 ... 91.938029 88.761490
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27 3712.0 85.091436 ... 83.666116 83.247783
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28 3840.0 81.019778 ... 79.965290 82.778440
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29 3968.0 85.992909 ... 84.974886 87.535103
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30 4096.0 93.531519 ... 90.200084 89.837839
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12 1792.0 72.983276 ... 62.441243 62.441243
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13 1920.0 68.098521 ... 70.172588 69.818184
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14 2048.0 73.584279 ... 74.235468 67.932543
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15 2176.0 82.813365 ... 79.855747 79.540109
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16 2304.0 68.643310 ... 73.501144 73.275679
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17 2432.0 71.305746 ... 72.037087 81.908060
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18 2560.0 77.833728 ... 76.560748 76.560748
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19 2688.0 80.880718 ... 81.752274 83.552988
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20 2816.0 81.981598 ... 79.298560 78.442822
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21 2944.0 81.564701 ... 77.505492 77.265163
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22 3072.0 81.355034 ... 82.782312 82.903517
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23 3200.0 81.321474 ... 85.906037 87.671229
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24 3328.0 82.748617 ... 81.994643 82.275764
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25 3456.0 81.683457 ... 81.932484 84.244062
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26 3584.0 87.211821 ... 87.722333 91.284657
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27 3712.0 85.675250 ... 82.017526 86.267139
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28 3840.0 84.485870 ... 86.738820 86.602979
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29 3968.0 92.864488 ... 87.284643 87.159957
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30 4096.0 93.531519 ... 88.885914 88.827088
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[31 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 19.244 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 13.178 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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@@ -370,7 +370,7 @@ to explore the <cite>triton/language/random</cite> folder!</p>
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<dd><p>Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, JMLR 2014</p>
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</dd>
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</dl>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.306 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.189 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-04-low-memory-dropout-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/c9aed78977a4c05741d675a38dde3d7d/04-low-memory-dropout.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">04-low-memory-dropout.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>04:43.861</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
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<p><strong>03:36.949</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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@@ -183,19 +183,19 @@
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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>02:19.244</p></td>
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<td><p>02:13.178</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>01:14.917</p></td>
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<td><p>01:12.586</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>01:09.393</p></td>
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<td><p>00:10.996</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="04-low-memory-dropout.html#sphx-glr-getting-started-tutorials-04-low-memory-dropout-py"><span class="std std-ref">Low-Memory Dropout</span></a> (<code class="docutils literal notranslate"><span class="pre">04-low-memory-dropout.py</span></code>)</p></td>
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<td><p>00:00.306</p></td>
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<td><p>00:00.189</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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