diff --git a/python/examples/tutorials/mat_mul.py b/python/examples/tutorials/mat_mul.py
index a8230207f..75650a6cb 100644
--- a/python/examples/tutorials/mat_mul.py
+++ b/python/examples/tutorials/mat_mul.py
@@ -16,16 +16,14 @@ class _dot(torch.autograd.Function):
         // accumulator
         float c[TM, TN] = 0;
 
-        //pointers
+        // pointers
         TYPE* pa[TM, TK] = A + rk[newaxis, :] * 1 + rm[:, newaxis] * lda;
         TYPE* pb[TK, TN] = B + rk[:, newaxis] * ldb + rn[newaxis, :] * 1;
 
         for(int k=K; k>0; k-=TK) {
             TYPE a[TM, TK] = *pa;
             TYPE b[TK, TN] = *pb;
-
             c += a @ b;
-
             pa = pa + TK * 1;
             pb = pb + TK * ldb;
         }
@@ -40,32 +38,35 @@ class _dot(torch.autograd.Function):
         c = _dot._call(a,b)
         return c
 
+    
+    kernel = dict()
 
     @staticmethod
     def _call(a, b):
+        # shapes
         M, K = a.shape
         K, N = b.shape
-
+        # leading dimension
         lda = K
         ldb = N
         ldc = N
-
         dtype = a.dtype
-
+        # create kernel if necessary
+        if dtype not in _dot.kernel:
+            defines = {
+                'TYPE' : dtype,
+                'TM'   : [64, 128],
+                'TN'   : [64, 128],
+                'TK'   : [8, 16],
+            }
+            _dot.kernel[dtype] = triton.kernel(_dot.src, num_warps=[2, 4], defines=defines)
+        kernel = _dot.kernel[dtype]
+        # allocate output
         c = triton.empty([M,N], dtype=dtype)
-
-        grid = lambda opt: [triton.cdiv(M, opt.d('TM')), triton.cdiv(N, opt.d('TN'))]
-
-        defines= {
-            'TYPE' : dtype,
-            'TM'   : [32,64,128],
-            'TN'   : [32,64,128],
-            'TK'   : [8],
-        }
-
-        _dot.kernel = triton.kernel(_dot.src, defines=defines)
-        _dot.kernel(a, b, c, M, N, K, lda, ldb, ldc,
-                        grid=grid, num_warps=4, defines=defines)
+        # enqueue
+        grid = lambda opt: [triton.cdiv(M, opt.d('TM')), 
+                            triton.cdiv(N, opt.d('TN'))]
+        kernel(a, b, c, M, N, K, lda, ldb, ldc, grid=grid)
         return c
 
 
@@ -81,4 +82,4 @@ b = torch.rand((K, N)).cuda()
 zc  = torch.matmul(a,b)
 zc_ = dot(a,b)
 
-print(torch.allclose(zc, zc_))
+print(torch.allclose(zc, zc_))
\ No newline at end of file