[GH-PAGES] Updated website
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@@ -459,37 +459,37 @@ 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 ... 3.276800 2.978909
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1 384.0 7.372800 ... 8.507077 8.507077
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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 ... 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 ... 35.389441 34.028308
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5 896.0 39.025776 ... 40.140799 39.025776
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4 768.0 31.597714 ... 34.028308 34.028308
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5 896.0 37.971025 ... 40.140799 39.025776
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6 1024.0 49.932191 ... 53.773130 52.428801
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7 1152.0 45.242181 ... 48.161033 47.396572
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8 1280.0 51.200001 ... 57.690139 57.690139
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9 1408.0 64.138541 ... 68.147202 66.485074
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10 1536.0 80.430545 ... 81.355034 78.643199
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11 1664.0 63.372618 ... 63.372618 62.492442
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10 1536.0 79.526831 ... 81.355034 78.643199
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11 1664.0 62.929456 ... 63.372618 62.492442
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12 1792.0 72.983276 ... 73.460287 59.467852
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13 1920.0 68.776119 ... 71.626943 70.892307
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14 2048.0 73.262953 ... 78.398206 76.959706
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15 2176.0 83.155572 ... 87.876193 85.998493
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16 2304.0 68.251065 ... 78.064941 77.057651
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17 2432.0 71.305746 ... 86.711310 84.115159
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18 2560.0 77.833728 ... 82.539044 81.512437
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19 2688.0 83.737433 ... 90.966561 89.044730
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20 2816.0 83.712490 ... 84.523664 83.712490
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21 2944.0 82.784108 ... 83.060049 82.646820
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22 3072.0 82.661468 ... 90.020831 87.787755
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23 3200.0 85.219705 ... 96.822991 95.952022
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24 3328.0 83.613586 ... 86.528001 84.200347
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25 3456.0 81.849303 ... 91.928814 88.595129
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26 3584.0 86.540320 ... 99.463928 91.563533
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27 3712.0 82.355598 ... 89.154253 81.283434
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28 3840.0 84.874902 ... 93.722032 85.399230
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29 3968.0 93.469117 ... 89.198780 86.175099
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30 4096.0 92.436452 ... 86.313653 82.241256
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13 1920.0 69.467336 ... 71.257735 70.892307
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14 2048.0 73.262953 ... 78.033565 77.314362
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15 2176.0 83.500614 ... 87.876193 85.998493
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16 2304.0 68.446623 ... 77.810656 77.057651
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17 2432.0 71.305746 ... 86.711310 85.393507
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18 2560.0 77.833728 ... 82.539044 81.715711
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19 2688.0 83.552988 ... 90.966561 88.422041
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20 2816.0 84.035084 ... 84.687779 83.712490
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21 2944.0 79.104810 ... 84.040530 82.646820
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22 3072.0 78.972252 ... 89.593522 87.924073
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23 3200.0 83.116885 ... 96.240602 94.674553
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24 3328.0 82.369902 ... 85.398926 84.795401
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25 3456.0 81.108217 ... 92.562076 85.767626
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26 3584.0 87.296493 ... 97.628001 97.416461
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27 3712.0 84.230479 ... 88.718781 87.552452
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28 3840.0 80.313725 ... 90.835321 86.875096
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29 3968.0 89.525997 ... 85.479929 89.855624
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30 4096.0 91.616198 ... 92.214171 89.359338
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[31 rows x 5 columns]
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@@ -499,7 +499,7 @@ 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:** ( 6 minutes 2.032 seconds)
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**Total running time of the script:** ( 6 minutes 6.178 seconds)
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.. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:
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