[TUTORIALS] Add multicta tutorial

python/tutorials/gluon/08-warp-specialization.py

@@ -627,19 +627,19 @@ def test_matmul_warp_specialized(M, N, K, BLOCK_M, BLOCK_N, BLOCK_K, num_buffers
         A = torch.randn(M, K, device="cuda", dtype=torch.float16)
         B = torch.randn(K, N, device="cuda", dtype=torch.float16)
         BT = B.T.contiguous()
-        r0 = as_flops(triton.testing.do_bench_cudagraph(lambda: matmul_warp_specialized(A, B, C, **args)))
+        r0 = as_flops(triton.testing.do_bench(lambda: matmul_warp_specialized(A, B, C, **args)))
         r1 = as_flops(triton.testing.do_bench(lambda: cublas.matmul(A, BT, C)))
         print(f"{K:>5} {r0:>17.2f} {r1:>9.2f}")
 
 # %%
 #     K  warp-specialized    cublas
-#   512           1160.28   1130.67
-#  1024           1249.69   1148.52
-#  2048           1347.18   1261.59
-#  4096           1390.95   1299.38
-#  8192           1350.01   1401.10
-# 16384           1448.14   1508.76
+#   512           1004.18   1191.77
+#  1024           1182.61   1334.85
+#  2048           1313.71   1400.35
+#  4096           1317.58   1432.32
+#  8192           1291.56   1301.11
+# 16384           1256.74   1335.24
 #
-# Much better! We are beating cublas on small K, even though there is still lots
-# of tuning we can do to improve performance. On Blackwell, warp specialization
-# is critical for achieving peak performance.
+# Much better! We are now quite competitive with cublas.
+# We will show in tutorial 14-multicta.py how we can use multicta and a few other
+# tricks to consistently beat cublas in a wide range of shapes.