[GH-PAGES] Updated website
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@@ -234,7 +234,7 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
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0 4096.0 9.600000 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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3 32768.0 63.999998 63.999998
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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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@@ -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 11.078 seconds)
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**Total running time of the script:** ( 0 minutes 11.005 seconds)
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.. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:
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@@ -308,9 +308,9 @@ We will then compare its performance against (1) :code:`torch.softmax` and (2) t
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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 198.854847
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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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95 12416.0 810.840807 412.149375 198.954424
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96 12544.0 810.925276 412.971190 199.209928
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97 12672.0 809.389265 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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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 1 minutes 12.572 seconds)
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**Total running time of the script:** ( 1 minutes 12.592 seconds)
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.. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:
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@@ -462,37 +462,37 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
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matmul-performance:
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M cuBLAS ... Triton Triton (+ LeakyReLU)
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0 256.0 2.730667 ... 2.978909 2.978909
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1 384.0 7.372800 ... 8.507077 8.507077
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0 256.0 2.730667 ... 3.276800 2.978909
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1 384.0 7.372800 ... 8.507077 7.899428
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2 512.0 14.563555 ... 15.420235 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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6 1024.0 49.932191 ... 52.428801 52.428801
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6 1024.0 49.932191 ... 53.773130 52.428801
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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 ... 65.684049 64.902096
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10 1536.0 80.430545 ... 76.933564 76.106321
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11 1664.0 63.372618 ... 62.492442 62.492442
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9 1408.0 64.138541 ... 64.902096 64.138541
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10 1536.0 79.526831 ... 76.106321 76.106321
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11 1664.0 62.929456 ... 62.492442 62.061463
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12 1792.0 72.983276 ... 70.246402 69.810085
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13 1920.0 69.467336 ... 70.172588 70.172588
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14 2048.0 73.584279 ... 75.234154 73.908442
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15 2176.0 82.813365 ... 78.916269 80.173899
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16 2304.0 68.446623 ... 73.501144 73.275679
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17 2432.0 71.125224 ... 82.388456 82.388456
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18 2560.0 77.833728 ... 77.283019 76.560748
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19 2688.0 83.369354 ... 81.053536 82.642823
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20 2816.0 82.290955 ... 79.443003 77.882512
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21 2944.0 81.698415 ... 79.356738 78.481940
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22 3072.0 82.420822 ... 82.903517 82.420822
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23 3200.0 81.424937 ... 84.544253 88.765601
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24 3328.0 83.905938 ... 87.368079 82.275764
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25 3456.0 81.108217 ... 86.134151 85.223646
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26 3584.0 83.177979 ... 94.548254 94.847460
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27 3712.0 81.615477 ... 83.526206 84.159518
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28 3840.0 81.557524 ... 81.557524 83.972664
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29 3968.0 86.175099 ... 82.840416 84.096442
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30 4096.0 92.755862 ... 89.717734 89.538177
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13 1920.0 69.467336 ... 70.530615 70.530615
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14 2048.0 73.908442 ... 75.234154 74.898285
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15 2176.0 83.500614 ... 80.173899 78.302130
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16 2304.0 68.251065 ... 73.275679 73.275679
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17 2432.0 71.125224 ... 81.669953 81.908060
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18 2560.0 77.649287 ... 76.740048 76.560748
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19 2688.0 83.004501 ... 80.708630 83.552988
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20 2816.0 82.446516 ... 79.443003 77.882512
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21 2944.0 81.967162 ... 80.122235 78.979452
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22 3072.0 80.890151 ... 83.886078 83.514905
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23 3200.0 81.321474 ... 89.260810 88.642656
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24 3328.0 82.939284 ... 86.841126 84.200347
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25 3456.0 81.932484 ... 80.702267 85.043848
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26 3584.0 87.296493 ... 92.126428 85.392267
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27 3712.0 85.163978 ... 82.084920 87.018592
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28 3840.0 85.070769 ... 87.632333 88.332269
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29 3968.0 92.864488 ... 84.038524 83.980685
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30 4096.0 93.727466 ... 86.424811 85.271746
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[31 rows x 5 columns]
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@@ -502,7 +502,7 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 2 minutes 13.454 seconds)
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**Total running time of the script:** ( 2 minutes 6.363 seconds)
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.. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:
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@@ -5,12 +5,12 @@
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Computation times
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=================
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**03:37.105** total execution time for **getting-started_tutorials** files:
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**03:29.960** total execution time for **getting-started_tutorials** files:
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+---------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:13.454 | 0.0 MB |
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| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:06.363 | 0.0 MB |
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+---------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 01:12.572 | 0.0 MB |
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| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 01:12.592 | 0.0 MB |
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+---------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 00:11.078 | 0.0 MB |
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| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 00:11.005 | 0.0 MB |
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+---------------------------------------------------------------------------------------------------------+-----------+--------+
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