[GH-PAGES] Updated website
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@@ -458,37 +458,37 @@ 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 2.978909 ... 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 15.420235
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3 640.0 22.260869 ... 24.380953 24.380953
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0 256.0 2.978909 ... 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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3 640.0 23.272727 ... 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 ... 39.025776 39.025776
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6 1024.0 49.932191 ... 52.428801 52.428801
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5 896.0 39.025776 ... 40.140799 39.025776
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6 1024.0 51.150050 ... 52.428801 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 56.888887
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9 1408.0 64.138541 ... 67.305878 66.485074
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10 1536.0 79.526831 ... 79.526831 79.526831
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11 1664.0 62.929456 ... 62.929456 62.492442
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12 1792.0 72.983276 ... 72.047592 71.588687
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13 1920.0 69.120002 ... 70.172588 69.818184
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14 2048.0 73.262953 ... 76.608294 76.260072
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15 2176.0 83.155572 ... 85.998493 85.632545
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16 2304.0 68.251065 ... 77.057651 76.809875
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17 2432.0 71.487187 ... 84.877538 84.367759
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18 2560.0 78.019048 ... 81.108913 80.511054
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19 2688.0 83.186525 ... 90.102270 89.044736
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20 2816.0 81.674548 ... 82.759409 82.759409
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21 2944.0 82.921853 ... 81.832567 81.967162
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22 3072.0 81.707223 ... 88.060814 88.750943
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23 3200.0 80.503145 ... 87.791493 91.888011
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24 3328.0 79.901550 ... 84.895397 84.695641
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25 3456.0 81.849303 ... 91.407671 91.200871
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26 3584.0 84.427601 ... 90.458141 96.579370
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27 3712.0 85.748791 ... 86.980708 87.170458
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28 3840.0 79.334289 ... 91.777595 84.292684
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29 3968.0 90.859224 ... 84.915752 90.994735
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30 4096.0 86.424811 ... 84.735086 91.678778
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10 1536.0 80.430545 ... 79.526831 78.643199
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11 1664.0 62.929456 ... 62.492442 62.061463
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12 1792.0 72.512412 ... 72.047592 71.588687
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13 1920.0 68.776119 ... 70.530615 69.818184
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14 2048.0 73.584279 ... 76.608294 76.608294
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15 2176.0 83.500614 ... 85.998493 85.632545
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16 2304.0 68.643310 ... 76.809875 76.563695
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17 2432.0 71.487187 ... 83.119713 85.134737
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18 2560.0 78.019048 ... 80.511054 80.511054
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19 2688.0 83.737433 ... 89.888756 89.464755
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20 2816.0 82.602666 ... 82.759409 82.602666
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21 2944.0 82.102191 ... 82.509987 82.784108
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22 3072.0 82.661468 ... 87.924073 85.275760
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23 3200.0 84.993363 ... 95.665176 94.674553
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24 3328.0 82.939284 ... 83.130825 84.596116
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25 3456.0 81.766291 ... 91.097818 84.597660
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26 3584.0 84.905939 ... 93.759530 87.296493
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27 3712.0 84.159518 ... 86.341700 86.867254
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28 3840.0 84.874902 ... 92.236860 84.552479
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29 3968.0 92.302520 ... 84.098250 90.791620
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30 4096.0 86.426548 ... 85.434583 91.180520
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[31 rows x 5 columns]
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@@ -498,7 +498,7 @@ 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:** ( 5 minutes 59.250 seconds)
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**Total running time of the script:** ( 6 minutes 6.796 seconds)
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.. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:
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