diff --git a/_downloads/662999063954282841dc90b8945f85ce/tutorials_jupyter.zip b/_downloads/662999063954282841dc90b8945f85ce/tutorials_jupyter.zip
index ee4ec089f..f534b81d1 100644
Binary files a/_downloads/662999063954282841dc90b8945f85ce/tutorials_jupyter.zip and b/_downloads/662999063954282841dc90b8945f85ce/tutorials_jupyter.zip differ
diff --git a/_downloads/763344228ae6bc253ed1a6cf586aa30d/tutorials_python.zip b/_downloads/763344228ae6bc253ed1a6cf586aa30d/tutorials_python.zip
index 8ccde5d2a..a993026c2 100644
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diff --git a/_images/sphx_glr_01-vector-add_001.png b/_images/sphx_glr_01-vector-add_001.png
index b5835159f..a6710a55f 100644
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diff --git a/_images/sphx_glr_01-vector-add_thumb.png b/_images/sphx_glr_01-vector-add_thumb.png
index 598456739..04b59a7e4 100644
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diff --git a/_images/sphx_glr_02-fused-softmax_001.png b/_images/sphx_glr_02-fused-softmax_001.png
index 681b5e657..351071914 100644
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diff --git a/_images/sphx_glr_02-fused-softmax_thumb.png b/_images/sphx_glr_02-fused-softmax_thumb.png
index c90c8669f..4b36de9bd 100644
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diff --git a/_images/sphx_glr_03-matrix-multiplication_001.png b/_images/sphx_glr_03-matrix-multiplication_001.png
index d49f278c7..68310cbe6 100644
Binary files a/_images/sphx_glr_03-matrix-multiplication_001.png and b/_images/sphx_glr_03-matrix-multiplication_001.png differ
diff --git a/_images/sphx_glr_03-matrix-multiplication_thumb.png b/_images/sphx_glr_03-matrix-multiplication_thumb.png
index 7fb5aca1e..f214a566a 100644
Binary files a/_images/sphx_glr_03-matrix-multiplication_thumb.png and b/_images/sphx_glr_03-matrix-multiplication_thumb.png differ
diff --git a/_sources/getting-started/tutorials/01-vector-add.rst.txt b/_sources/getting-started/tutorials/01-vector-add.rst.txt
index da55befe2..b852d5f0e 100644
--- a/_sources/getting-started/tutorials/01-vector-add.rst.txt
+++ b/_sources/getting-started/tutorials/01-vector-add.rst.txt
@@ -216,10 +216,10 @@ We can now run the decorated function above. Pass `show_plots=True` to see the p
 
     vector-add-performance:
                size      Triton       Torch
-    0        4096.0    8.000000    9.600000
+    0        4096.0    9.540372    9.600000
     1        8192.0   19.200000   19.200000
     2       16384.0   38.400001   38.400001
-    3       32768.0   76.800002   76.800002
+    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  384.000001
@@ -239,7 +239,7 @@ We can now run the decorated function above. Pass `show_plots=True` to see the p
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  10.970 seconds)
+   **Total running time of the script:** ( 0 minutes  11.008 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:
diff --git a/_sources/getting-started/tutorials/02-fused-softmax.rst.txt b/_sources/getting-started/tutorials/02-fused-softmax.rst.txt
index 2e79d68b3..96f725a7f 100644
--- a/_sources/getting-started/tutorials/02-fused-softmax.rst.txt
+++ b/_sources/getting-started/tutorials/02-fused-softmax.rst.txt
@@ -261,17 +261,17 @@ We will then compare its performance against (1) :code:`torch.softmax` and (2) t
 
     softmax-performance:
               N      Triton  Torch (native)  Torch (jit)
-    0     256.0  512.000001      546.133347   273.066674
+    0     256.0  512.000001      546.133347   264.258068
     1     384.0  585.142862      585.142862   267.130429
     2     512.0  630.153853      606.814814   264.258068
-    3     640.0  682.666684      640.000002   269.473696
+    3     640.0  682.666684      640.000002   265.974036
     4     768.0  702.171410      664.216187   273.066663
     ..      ...         ...             ...          ...
-    93  12160.0  812.359066      405.755985   329.483481
+    93  12160.0  812.359066      406.179533   329.483481
     94  12288.0  812.429770      415.661740   329.602681
-    95  12416.0  810.840807      411.722274   329.173158
+    95  12416.0  810.840807      412.149375   328.900662
     96  12544.0  810.925276      412.971190   329.292871
-    97  12672.0  811.007961      412.097543   329.142870
+    97  12672.0  811.007961      412.516771   329.142870
 
     [98 rows x 4 columns]
 
@@ -290,7 +290,7 @@ In the above plot, we can see that:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  8.170 seconds)
+   **Total running time of the script:** ( 1 minutes  8.191 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:
diff --git a/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt b/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
index 7d41e3168..2d6335de7 100644
--- a/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
+++ b/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
@@ -371,37 +371,37 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
     matmul-performance:
              M     cuBLAS  ...     Triton  Triton (+ LeakyReLU)
     0    128.0   0.455111  ...   0.512000              0.512000
-    1    256.0   2.978909  ...   3.276800              3.276800
+    1    256.0   2.978909  ...   2.978909              2.978909
     2    384.0   7.372800  ...   8.507077              8.507077
-    3    512.0  14.563555  ...  16.384000             15.420235
-    4    640.0  22.260869  ...  23.272727             23.272727
+    3    512.0  14.563555  ...  15.420235             15.420235
+    4    640.0  22.260869  ...  24.380953             24.380953
     5    768.0  32.768000  ...  34.028308             34.028308
-    6    896.0  37.971025  ...  39.025776             37.971025
-    7   1024.0  51.150050  ...  52.428801             52.428801
+    6    896.0  39.025776  ...  39.025776             39.025776
+    7   1024.0  49.932191  ...  52.428801             52.428801
     8   1152.0  44.566925  ...  45.938215             45.938215
     9   1280.0  51.200001  ...  56.109587             56.109587
-    10  1408.0  64.138541  ...  64.902096             58.621246
-    11  1536.0  77.778988  ...  76.106321             75.296679
+    10  1408.0  64.138541  ...  65.684049             58.621246
+    11  1536.0  79.526831  ...  75.296679             75.296679
     12  1664.0  62.929456  ...  61.636381             61.636381
-    13  1792.0  72.983276  ...  68.953520             68.533074
-    14  1920.0  69.120002  ...  69.467336             69.120002
-    15  2048.0  73.584279  ...  75.573044             74.898285
-    16  2176.0  82.137338  ...  80.173899             78.916269
-    17  2304.0  68.446623  ...  73.275679             72.828879
-    18  2432.0  71.305746  ...  80.041209             79.362895
-    19  2560.0  78.019048  ...  76.920185             76.382283
-    20  2688.0  81.928846  ...  80.708630             83.369354
-    21  2816.0  82.446516  ...  78.868366             78.584162
-    22  2944.0  82.102191  ...  79.356738             78.854483
-    23  3072.0  81.121923  ...  80.544956             82.301023
-    24  3200.0  84.432717  ...  89.887639             87.431696
-    25  3328.0  81.622783  ...  85.297742             86.528001
-    26  3456.0  79.823334  ...  86.134151             81.683457
-    27  3584.0  85.797134  ...  96.063450             87.808000
-    28  3712.0  85.163978  ...  83.736248             81.480261
-    29  3840.0  83.655065  ...  82.716526             84.228485
-    30  3968.0  89.003603  ...  82.337339             80.017334
-    31  4096.0  93.271527  ...  90.443212             87.324485
+    13  1792.0  72.983276  ...  68.533074             68.533074
+    14  1920.0  66.461539  ...  67.106797             69.467336
+    15  2048.0  73.584279  ...  75.573044             75.573044
+    16  2176.0  81.472263  ...  78.608000             80.173899
+    17  2304.0  68.446623  ...  72.387489             72.607513
+    18  2432.0  71.125224  ...  81.197876             81.433227
+    19  2560.0  78.019048  ...  75.676673             76.920185
+    20  2688.0  81.752274  ...  84.483418             85.051697
+    21  2816.0  81.981598  ...  79.298560             79.587973
+    22  2944.0  82.102191  ...  80.902653             78.235527
+    23  3072.0  81.238312  ...  84.135370             83.269271
+    24  3200.0  83.224970  ...  83.989503             89.887639
+    25  3328.0  83.905938  ...  83.323259             84.298943
+    26  3456.0  82.266905  ...  86.689860             86.503829
+    27  3584.0  84.033077  ...  91.656871             96.475743
+    28  3712.0  85.528545  ...  83.526206             88.561477
+    29  3840.0  82.716526  ...  81.738356             85.399230
+    30  3968.0  92.793868  ...  86.849777             87.159957
+    31  4096.0  93.531519  ...  92.056052             92.372834
 
     [32 rows x 5 columns]
 
@@ -411,7 +411,7 @@ 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  16.316 seconds)
+   **Total running time of the script:** ( 2 minutes  16.809 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:
diff --git a/_sources/getting-started/tutorials/sg_execution_times.rst.txt b/_sources/getting-started/tutorials/sg_execution_times.rst.txt
index 7af0eb1c4..55b339f1a 100644
--- a/_sources/getting-started/tutorials/sg_execution_times.rst.txt
+++ b/_sources/getting-started/tutorials/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**03:35.456** total execution time for **getting-started_tutorials** files:
+**03:36.008** total execution time for **getting-started_tutorials** files:
 
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:16.316 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:16.809 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 01:08.170 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 01:08.191 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 00:10.970 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 00:11.008 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/getting-started/tutorials/01-vector-add.html b/getting-started/tutorials/01-vector-add.html
index dc11a1326..c0112f0de 100644
--- a/getting-started/tutorials/01-vector-add.html
+++ b/getting-started/tutorials/01-vector-add.html
@@ -305,10 +305,10 @@ 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    8.000000    9.600000
+0        4096.0    9.540372    9.600000
 1        8192.0   19.200000   19.200000
 2       16384.0   38.400001   38.400001
-3       32768.0   76.800002   76.800002
+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  384.000001
@@ -323,7 +323,7 @@ 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  10.970 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  11.008 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>
diff --git a/getting-started/tutorials/02-fused-softmax.html b/getting-started/tutorials/02-fused-softmax.html
index 94be4754d..304b44e7e 100644
--- a/getting-started/tutorials/02-fused-softmax.html
+++ b/getting-started/tutorials/02-fused-softmax.html
@@ -346,17 +346,17 @@ We will then compare its performance against (1) <code class="code docutils lite
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>softmax-performance:
           N      Triton  Torch (native)  Torch (jit)
-0     256.0  512.000001      546.133347   273.066674
+0     256.0  512.000001      546.133347   264.258068
 1     384.0  585.142862      585.142862   267.130429
 2     512.0  630.153853      606.814814   264.258068
-3     640.0  682.666684      640.000002   269.473696
+3     640.0  682.666684      640.000002   265.974036
 4     768.0  702.171410      664.216187   273.066663
 ..      ...         ...             ...          ...
-93  12160.0  812.359066      405.755985   329.483481
+93  12160.0  812.359066      406.179533   329.483481
 94  12288.0  812.429770      415.661740   329.602681
-95  12416.0  810.840807      411.722274   329.173158
+95  12416.0  810.840807      412.149375   328.900662
 96  12544.0  810.925276      412.971190   329.292871
-97  12672.0  811.007961      412.097543   329.142870
+97  12672.0  811.007961      412.516771   329.142870
 
 [98 rows x 4 columns]
 </pre></div>
@@ -370,7 +370,7 @@ This means that – when temporary data is too large to fit entirely in the GPU
 Note that our Triton kernel is not only faster than PyTorch’s CUDA kernel, it is also <strong>easier to read, understand and maintain</strong>.</p></li>
 </ul>
 </div></blockquote>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  8.170 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  8.191 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>
diff --git a/getting-started/tutorials/03-matrix-multiplication.html b/getting-started/tutorials/03-matrix-multiplication.html
index 5d9abaef5..2dfa6c531 100644
--- a/getting-started/tutorials/03-matrix-multiplication.html
+++ b/getting-started/tutorials/03-matrix-multiplication.html
@@ -476,42 +476,42 @@ tensor(True, device=&#39;cuda:0&#39;)
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>matmul-performance:
          M     cuBLAS  ...     Triton  Triton (+ LeakyReLU)
 0    128.0   0.455111  ...   0.512000              0.512000
-1    256.0   2.978909  ...   3.276800              3.276800
+1    256.0   2.978909  ...   2.978909              2.978909
 2    384.0   7.372800  ...   8.507077              8.507077
-3    512.0  14.563555  ...  16.384000             15.420235
-4    640.0  22.260869  ...  23.272727             23.272727
+3    512.0  14.563555  ...  15.420235             15.420235
+4    640.0  22.260869  ...  24.380953             24.380953
 5    768.0  32.768000  ...  34.028308             34.028308
-6    896.0  37.971025  ...  39.025776             37.971025
-7   1024.0  51.150050  ...  52.428801             52.428801
+6    896.0  39.025776  ...  39.025776             39.025776
+7   1024.0  49.932191  ...  52.428801             52.428801
 8   1152.0  44.566925  ...  45.938215             45.938215
 9   1280.0  51.200001  ...  56.109587             56.109587
-10  1408.0  64.138541  ...  64.902096             58.621246
-11  1536.0  77.778988  ...  76.106321             75.296679
+10  1408.0  64.138541  ...  65.684049             58.621246
+11  1536.0  79.526831  ...  75.296679             75.296679
 12  1664.0  62.929456  ...  61.636381             61.636381
-13  1792.0  72.983276  ...  68.953520             68.533074
-14  1920.0  69.120002  ...  69.467336             69.120002
-15  2048.0  73.584279  ...  75.573044             74.898285
-16  2176.0  82.137338  ...  80.173899             78.916269
-17  2304.0  68.446623  ...  73.275679             72.828879
-18  2432.0  71.305746  ...  80.041209             79.362895
-19  2560.0  78.019048  ...  76.920185             76.382283
-20  2688.0  81.928846  ...  80.708630             83.369354
-21  2816.0  82.446516  ...  78.868366             78.584162
-22  2944.0  82.102191  ...  79.356738             78.854483
-23  3072.0  81.121923  ...  80.544956             82.301023
-24  3200.0  84.432717  ...  89.887639             87.431696
-25  3328.0  81.622783  ...  85.297742             86.528001
-26  3456.0  79.823334  ...  86.134151             81.683457
-27  3584.0  85.797134  ...  96.063450             87.808000
-28  3712.0  85.163978  ...  83.736248             81.480261
-29  3840.0  83.655065  ...  82.716526             84.228485
-30  3968.0  89.003603  ...  82.337339             80.017334
-31  4096.0  93.271527  ...  90.443212             87.324485
+13  1792.0  72.983276  ...  68.533074             68.533074
+14  1920.0  66.461539  ...  67.106797             69.467336
+15  2048.0  73.584279  ...  75.573044             75.573044
+16  2176.0  81.472263  ...  78.608000             80.173899
+17  2304.0  68.446623  ...  72.387489             72.607513
+18  2432.0  71.125224  ...  81.197876             81.433227
+19  2560.0  78.019048  ...  75.676673             76.920185
+20  2688.0  81.752274  ...  84.483418             85.051697
+21  2816.0  81.981598  ...  79.298560             79.587973
+22  2944.0  82.102191  ...  80.902653             78.235527
+23  3072.0  81.238312  ...  84.135370             83.269271
+24  3200.0  83.224970  ...  83.989503             89.887639
+25  3328.0  83.905938  ...  83.323259             84.298943
+26  3456.0  82.266905  ...  86.689860             86.503829
+27  3584.0  84.033077  ...  91.656871             96.475743
+28  3712.0  85.528545  ...  83.526206             88.561477
+29  3840.0  82.716526  ...  81.738356             85.399230
+30  3968.0  92.793868  ...  86.849777             87.159957
+31  4096.0  93.531519  ...  92.056052             92.372834
 
 [32 rows x 5 columns]
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