[GH-PAGES] Updated website
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@@ -231,13 +231,13 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
|
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|
||||
vector-add-performance:
|
||||
size Triton Torch
|
||||
0 4096.0 9.540372 9.600000
|
||||
0 4096.0 9.600000 9.600000
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||||
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2 16384.0 31.999999 38.400001
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2 16384.0 38.400001 38.400001
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3 32768.0 63.999998 63.999998
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4 65536.0 127.999995 127.999995
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6 262144.0 384.000001 384.000001
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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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@@ -254,7 +254,7 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
|
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.. rst-class:: sphx-glr-timing
|
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**Total running time of the script:** ( 0 minutes 10.969 seconds)
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**Total running time of the script:** ( 0 minutes 10.961 seconds)
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.. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:
|
||||
|
@@ -306,11 +306,11 @@ We will then compare its performance against (1) :code:`torch.softmax` and (2) t
|
||||
3 640.0 682.666684 640.000002 160.000000
|
||||
4 768.0 702.171410 664.216187 163.839992
|
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.. ... ... ... ...
|
||||
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|
||||
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|
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|
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||||
93 12160.0 812.359066 406.179533 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 198.954424
|
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96 12544.0 810.925276 412.546756 199.209928
|
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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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@@ -328,7 +328,7 @@ In the above plot, we can see that:
|
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|
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.. rst-class:: sphx-glr-timing
|
||||
|
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**Total running time of the script:** ( 1 minutes 12.674 seconds)
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**Total running time of the script:** ( 1 minutes 12.587 seconds)
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.. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:
|
||||
|
@@ -462,37 +462,37 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
|
||||
|
||||
matmul-performance:
|
||||
M cuBLAS ... Triton Triton (+ LeakyReLU)
|
||||
0 256.0 2.730667 ... 3.276800 2.978909
|
||||
1 384.0 7.372800 ... 7.899428 7.899428
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0 256.0 2.978909 ... 3.276800 2.978909
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||||
1 384.0 7.372800 ... 8.507077 7.899428
|
||||
2 512.0 14.563555 ... 16.384000 16.384000
|
||||
3 640.0 22.260869 ... 24.380953 24.380953
|
||||
4 768.0 32.768000 ... 34.028308 34.028308
|
||||
4 768.0 32.768000 ... 35.389441 34.028308
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||||
5 896.0 39.025776 ... 40.140799 39.025776
|
||||
6 1024.0 49.932191 ... 53.773130 52.428801
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||||
7 1152.0 45.242181 ... 46.656000 46.656000
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||||
8 1280.0 51.200001 ... 56.888887 56.109587
|
||||
9 1408.0 64.138541 ... 64.902096 64.902096
|
||||
10 1536.0 80.430545 ... 76.106321 76.106321
|
||||
11 1664.0 63.372618 ... 62.492442 62.061463
|
||||
12 1792.0 72.983276 ... 69.379162 69.379162
|
||||
13 1920.0 69.120002 ... 70.172588 70.172588
|
||||
14 2048.0 73.908442 ... 75.234154 68.487170
|
||||
15 2176.0 82.813365 ... 81.143743 80.173899
|
||||
7 1152.0 44.566925 ... 46.656000 46.656000
|
||||
8 1280.0 51.200001 ... 56.109587 56.109587
|
||||
9 1408.0 64.138541 ... 64.902096 58.016903
|
||||
10 1536.0 79.526831 ... 76.106321 76.106321
|
||||
11 1664.0 62.929456 ... 62.061463 62.061463
|
||||
12 1792.0 72.983276 ... 69.810085 69.379162
|
||||
13 1920.0 68.098521 ... 70.530615 70.530615
|
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14 2048.0 73.908442 ... 74.898285 74.565406
|
||||
15 2176.0 83.155572 ... 79.855747 80.494588
|
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16 2304.0 68.446623 ... 73.728002 73.501144
|
||||
17 2432.0 71.305746 ... 82.147552 81.908060
|
||||
18 2560.0 77.649287 ... 77.465723 76.382283
|
||||
19 2688.0 80.880718 ... 82.106182 80.708630
|
||||
20 2816.0 82.602666 ... 80.320825 80.173175
|
||||
21 2944.0 82.784108 ... 79.356738 79.865439
|
||||
22 3072.0 81.707223 ... 83.025078 82.420822
|
||||
23 3200.0 79.601989 ... 84.768213 88.642656
|
||||
24 3328.0 82.939284 ... 87.368079 82.181847
|
||||
25 3456.0 80.300370 ... 84.775569 85.676480
|
||||
26 3584.0 84.905939 ... 94.947616 94.947616
|
||||
27 3712.0 84.230479 ... 89.755028 89.513749
|
||||
28 3840.0 85.399230 ... 84.036474 83.972664
|
||||
29 3968.0 88.295175 ... 87.787005 87.850207
|
||||
30 4096.0 93.336389 ... 83.313299 90.748973
|
||||
17 2432.0 71.305746 ... 80.963875 82.147552
|
||||
18 2560.0 78.019048 ... 77.283019 75.676673
|
||||
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|
||||
20 2816.0 83.873477 ... 77.056904 79.733474
|
||||
21 2944.0 81.832567 ... 79.737653 78.729910
|
||||
22 3072.0 81.943708 ... 84.135370 78.972252
|
||||
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|
||||
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|
||||
25 3456.0 81.932484 ... 83.719178 85.043848
|
||||
26 3584.0 84.986191 ... 93.370085 94.947616
|
||||
27 3712.0 85.528545 ... 89.835744 89.035062
|
||||
28 3840.0 84.809814 ... 88.900318 83.027026
|
||||
29 3968.0 86.116179 ... 87.913500 87.723894
|
||||
30 4096.0 93.662059 ... 89.657802 87.781379
|
||||
|
||||
[31 rows x 5 columns]
|
||||
|
||||
@@ -502,7 +502,7 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
|
||||
|
||||
.. rst-class:: sphx-glr-timing
|
||||
|
||||
**Total running time of the script:** ( 2 minutes 15.728 seconds)
|
||||
**Total running time of the script:** ( 2 minutes 16.756 seconds)
|
||||
|
||||
|
||||
.. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:
|
||||
|
@@ -5,12 +5,12 @@
|
||||
|
||||
Computation times
|
||||
=================
|
||||
**03:39.371** total execution time for **getting-started_tutorials** files:
|
||||
**03:40.304** total execution time for **getting-started_tutorials** files:
|
||||
|
||||
+---------------------------------------------------------------------------------------------------------+-----------+--------+
|
||||
| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:15.728 | 0.0 MB |
|
||||
| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:16.756 | 0.0 MB |
|
||||
+---------------------------------------------------------------------------------------------------------+-----------+--------+
|
||||
| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 01:12.674 | 0.0 MB |
|
||||
| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 01:12.587 | 0.0 MB |
|
||||
+---------------------------------------------------------------------------------------------------------+-----------+--------+
|
||||
| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 00:10.969 | 0.0 MB |
|
||||
| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 00:10.961 | 0.0 MB |
|
||||
+---------------------------------------------------------------------------------------------------------+-----------+--------+
|
||||
|
@@ -319,13 +319,13 @@ for different problem sizes.</p>
|
||||
<p class="sphx-glr-script-out">Out:</p>
|
||||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector-add-performance:
|
||||
size Triton Torch
|
||||
0 4096.0 9.540372 9.600000
|
||||
0 4096.0 9.600000 9.600000
|
||||
1 8192.0 19.200000 19.200000
|
||||
2 16384.0 31.999999 38.400001
|
||||
3 32768.0 63.999998 76.800002
|
||||
2 16384.0 38.400001 38.400001
|
||||
3 32768.0 63.999998 63.999998
|
||||
4 65536.0 127.999995 127.999995
|
||||
5 131072.0 219.428568 219.428568
|
||||
6 262144.0 341.333321 341.333321
|
||||
6 262144.0 384.000001 384.000001
|
||||
7 524288.0 472.615390 472.615390
|
||||
8 1048576.0 614.400016 614.400016
|
||||
9 2097152.0 722.823517 722.823517
|
||||
@@ -337,7 +337,7 @@ for different problem sizes.</p>
|
||||
15 134217728.0 851.577704 850.656574
|
||||
</pre></div>
|
||||
</div>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 10.969 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 10.961 seconds)</p>
|
||||
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-01-vector-add-py">
|
||||
<div class="sphx-glr-download sphx-glr-download-python docutils container">
|
||||
<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>
|
||||
|
@@ -391,11 +391,11 @@ We will then compare its performance against (1) <code class="code docutils lite
|
||||
3 640.0 682.666684 640.000002 160.000000
|
||||
4 768.0 702.171410 664.216187 163.839992
|
||||
.. ... ... ... ...
|
||||
93 12160.0 812.359066 406.179533 198.834951
|
||||
94 12288.0 812.429770 415.661740 199.096718
|
||||
95 12416.0 810.840807 412.149375 198.755369
|
||||
96 12544.0 809.290334 412.971190 199.012395
|
||||
97 12672.0 811.007961 412.097543 199.069228
|
||||
93 12160.0 812.359066 406.179533 199.038365
|
||||
94 12288.0 812.429770 415.661740 199.298541
|
||||
95 12416.0 810.840807 412.149375 198.954424
|
||||
96 12544.0 810.925276 412.546756 199.209928
|
||||
97 12672.0 811.007961 412.097543 199.264875
|
||||
|
||||
[98 rows x 4 columns]
|
||||
</pre></div>
|
||||
@@ -408,7 +408,7 @@ We will then compare its performance against (1) <code class="code docutils lite
|
||||
Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li>
|
||||
</ul>
|
||||
</div></blockquote>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 12.674 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 12.587 seconds)</p>
|
||||
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-02-fused-softmax-py">
|
||||
<div class="sphx-glr-download sphx-glr-download-python docutils container">
|
||||
<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>
|
||||
|
@@ -566,42 +566,42 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
|
||||
<p class="sphx-glr-script-out">Out:</p>
|
||||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>matmul-performance:
|
||||
M cuBLAS ... Triton Triton (+ LeakyReLU)
|
||||
0 256.0 2.730667 ... 3.276800 2.978909
|
||||
1 384.0 7.372800 ... 7.899428 7.899428
|
||||
0 256.0 2.978909 ... 3.276800 2.978909
|
||||
1 384.0 7.372800 ... 8.507077 7.899428
|
||||
2 512.0 14.563555 ... 16.384000 16.384000
|
||||
3 640.0 22.260869 ... 24.380953 24.380953
|
||||
4 768.0 32.768000 ... 34.028308 34.028308
|
||||
4 768.0 32.768000 ... 35.389441 34.028308
|
||||
5 896.0 39.025776 ... 40.140799 39.025776
|
||||
6 1024.0 49.932191 ... 53.773130 52.428801
|
||||
7 1152.0 45.242181 ... 46.656000 46.656000
|
||||
8 1280.0 51.200001 ... 56.888887 56.109587
|
||||
9 1408.0 64.138541 ... 64.902096 64.902096
|
||||
10 1536.0 80.430545 ... 76.106321 76.106321
|
||||
11 1664.0 63.372618 ... 62.492442 62.061463
|
||||
12 1792.0 72.983276 ... 69.379162 69.379162
|
||||
13 1920.0 69.120002 ... 70.172588 70.172588
|
||||
14 2048.0 73.908442 ... 75.234154 68.487170
|
||||
15 2176.0 82.813365 ... 81.143743 80.173899
|
||||
7 1152.0 44.566925 ... 46.656000 46.656000
|
||||
8 1280.0 51.200001 ... 56.109587 56.109587
|
||||
9 1408.0 64.138541 ... 64.902096 58.016903
|
||||
10 1536.0 79.526831 ... 76.106321 76.106321
|
||||
11 1664.0 62.929456 ... 62.061463 62.061463
|
||||
12 1792.0 72.983276 ... 69.810085 69.379162
|
||||
13 1920.0 68.098521 ... 70.530615 70.530615
|
||||
14 2048.0 73.908442 ... 74.898285 74.565406
|
||||
15 2176.0 83.155572 ... 79.855747 80.494588
|
||||
16 2304.0 68.446623 ... 73.728002 73.501144
|
||||
17 2432.0 71.305746 ... 82.147552 81.908060
|
||||
18 2560.0 77.649287 ... 77.465723 76.382283
|
||||
19 2688.0 80.880718 ... 82.106182 80.708630
|
||||
20 2816.0 82.602666 ... 80.320825 80.173175
|
||||
21 2944.0 82.784108 ... 79.356738 79.865439
|
||||
22 3072.0 81.707223 ... 83.025078 82.420822
|
||||
23 3200.0 79.601989 ... 84.768213 88.642656
|
||||
24 3328.0 82.939284 ... 87.368079 82.181847
|
||||
25 3456.0 80.300370 ... 84.775569 85.676480
|
||||
26 3584.0 84.905939 ... 94.947616 94.947616
|
||||
27 3712.0 84.230479 ... 89.755028 89.513749
|
||||
28 3840.0 85.399230 ... 84.036474 83.972664
|
||||
29 3968.0 88.295175 ... 87.787005 87.850207
|
||||
30 4096.0 93.336389 ... 83.313299 90.748973
|
||||
17 2432.0 71.305746 ... 80.963875 82.147552
|
||||
18 2560.0 78.019048 ... 77.283019 75.676673
|
||||
19 2688.0 83.369354 ... 81.401408 82.284288
|
||||
20 2816.0 83.873477 ... 77.056904 79.733474
|
||||
21 2944.0 81.832567 ... 79.737653 78.729910
|
||||
22 3072.0 81.943708 ... 84.135370 78.972252
|
||||
23 3200.0 84.880639 ... 90.014065 85.906037
|
||||
24 3328.0 83.034941 ... 86.736504 87.156532
|
||||
25 3456.0 81.932484 ... 83.719178 85.043848
|
||||
26 3584.0 84.986191 ... 93.370085 94.947616
|
||||
27 3712.0 85.528545 ... 89.835744 89.035062
|
||||
28 3840.0 84.809814 ... 88.900318 83.027026
|
||||
29 3968.0 86.116179 ... 87.913500 87.723894
|
||||
30 4096.0 93.662059 ... 89.657802 87.781379
|
||||
|
||||
[31 rows x 5 columns]
|
||||
</pre></div>
|
||||
</div>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 15.728 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 16.756 seconds)</p>
|
||||
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-03-matrix-multiplication-py">
|
||||
<div class="sphx-glr-download sphx-glr-download-python docutils container">
|
||||
<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>
|
||||
|
@@ -174,7 +174,7 @@
|
||||
|
||||
<div class="section" id="computation-times">
|
||||
<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>
|
||||
<p><strong>03:39.371</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<p><strong>03:40.304</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<table class="docutils align-default">
|
||||
<colgroup>
|
||||
<col style="width: 85%" />
|
||||
@@ -183,15 +183,15 @@
|
||||
</colgroup>
|
||||
<tbody>
|
||||
<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>
|
||||
<td><p>02:15.728</p></td>
|
||||
<td><p>02:16.756</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<td><p>01:12.674</p></td>
|
||||
<td><p>01:12.587</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<td><p>00:10.969</p></td>
|
||||
<td><p>00:10.961</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
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||||
</tbody>
|
||||
|