diff --git a/examples/cuda_matmul_opt.py b/examples/cuda_matmul_opt.py
index 35600ce2..121a2ca8 100644
--- a/examples/cuda_matmul_opt.py
+++ b/examples/cuda_matmul_opt.py
@@ -140,9 +140,7 @@ def sgemm_naive[
     B_t: T.memref(K, N, dtype),
     C_t: T.memref(M, N, dtype),
 ](A: A_t, B: B_t, C: C_t):
-    one = arith.constant(1.0, type=dtype)
     tmp = arith.constant(0, type=dtype)
-
     # this is from the example and it's basically a mistake
     # it increments the row for each adjacent thread id
     # uncomment the print to see
@@ -154,7 +152,7 @@ def sgemm_naive[
     for k, tmp in range_(K, iter_args=[tmp]):
         tmp += A[r, k] * B[k, c]
         tmp = yield tmp
-    C[r, c] = tmp + one
+    C[r, c] = tmp + 1
 
 
 @gpu.func
@@ -168,7 +166,7 @@ def sgemm_naive_row_order[
     B_t: T.memref(K, N, dtype),
     C_t: T.memref(M, N, dtype),
 ](A: A_t, B: B_t, C: C_t):
-    one = arith.constant(1.0, type=dtype)
+
     tmp = arith.constant(0, type=dtype)
 
     # increment along the cols (ie preserve row-order access)
@@ -180,7 +178,7 @@ def sgemm_naive_row_order[
     for k, tmp in range_(K, iter_args=[tmp]):
         tmp += A[r, k] * B[k, c]
         tmp = yield tmp
-    C[r, c] = tmp + one
+    C[r, c] = tmp + 1
 
 
 @gpu.func
@@ -202,7 +200,6 @@ def sgemm_coalesce[
     c = block_idx.y * BLOCK_SIZE + (tid % BLOCK_SIZE)
     # gpu.printf("tid: %ld: (%ld, %ld)\n", tid, r, c)
 
-    one = arith.constant(1.0, type=dtype)
     tmp = arith.constant(0, type=dtype)
 
     for k, tmp in range_(K, iter_args=[tmp]):
@@ -211,7 +208,7 @@ def sgemm_coalesce[
         # because there's enough scratch per SM to prefetch all the data each thread needs?
         tmp += A[r, k] * B[k, c]
         tmp = yield tmp
-    C[r, c] = tmp + one
+    C[r, c] = tmp + 1
 
 
 # So if you try to load something like:
@@ -266,7 +263,6 @@ def sgemm_coalesce_transpose_B[
     r = block_idx.x * BLOCK_SIZE + (tid / BLOCK_SIZE)
     c = block_idx.y * BLOCK_SIZE + (tid % BLOCK_SIZE)
 
-    one = arith.constant(1.0, type=dtype)
     tmp = arith.constant(0, type=dtype)
 
     for k, tmp in range_(K, iter_args=[tmp]):
@@ -275,7 +271,7 @@ def sgemm_coalesce_transpose_B[
         # but k now being on the row order dim doesn't help?
         tmp += A[r, k] * B[c, k]
         tmp = yield tmp
-    C[r, c] = tmp + one
+    C[r, c] = tmp + 1
 
 
 @gpu.func
@@ -333,9 +329,8 @@ def sgemm_shared_mem_block[
 
         tmp = yield tmp
 
-    one = arith.constant(1.0, type=dtype)
     C_ = C[c_row : c_row + BLOCK_SIZE, c_col : c_col + BLOCK_SIZE]
-    C_[thread_row, thread_col] = tmp + one
+    C_[thread_row, thread_col] = tmp + 1
 
 
 def prepare_non_tiled_kernel(ctx: MLIRContext, kernel, M, K, N, BLOCK_SIZE=32):
@@ -429,10 +424,9 @@ def sgemm_shared_mem_1d_block_tiling[
 
         gpu.barrier()
 
-    one = arith.constant(1.0, type=dtype)
     C_ = C[c_row : c_row + BM, c_col : c_col + BN]
     for res_idx in range_(TM):
-        C_[thread_row * TM + res_idx, thread_col] = thread_results[res_idx] + one
+        C_[thread_row * TM + res_idx, thread_col] = thread_results[res_idx] + 1
 
 
 @gpu.func
@@ -512,13 +506,12 @@ def sgemm_shared_mem_2d_block_tiling[
 
         gpu.barrier()
 
-    one = arith.constant(1.0, type=dtype)
     C_ = C[c_row : c_row + BM, c_col : c_col + BN]
 
     for res_idx_m in range_(TM):
         for res_idx_n in range_(TN):
             C_[thread_row * TM + res_idx_m, thread_col * TN + res_idx_n] = (
-                thread_results[res_idx_m, res_idx_n] + one
+                thread_results[res_idx_m, res_idx_n] + 1
             )
 
 
@@ -612,7 +605,6 @@ def sgemm_shared_mem_2d_block_tiling_vectorize[
 
         gpu.barrier()
 
-    one = arith.constant(1.0, type=dtype)
     C_ = C[c_row : c_row + BM, c_col : c_col + BN]
 
     for res_idx_m in range_(TM):
@@ -623,7 +615,7 @@ def sgemm_shared_mem_2d_block_tiling_vectorize[
                 [thread_row * TM + res_idx_m, thread_col * TN + res_idx_n],
             )
             for j in range(VECTOR_WIDTH):
-                tmp[j] = thread_results[res_idx_m, res_idx_n + j] + one
+                tmp[j] = thread_results[res_idx_m, res_idx_n + j] + 1
             vector.store(
                 tmp, C_, [thread_row * TM + res_idx_m, thread_col * TN + res_idx_n]
             )
@@ -771,8 +763,6 @@ def sgemm_warp_tiling[
 
         gpu.barrier()
 
-    one = arith.constant(1.0, type=dtype)
-
     for w_sub_row_idx in range_(WMITER):
         for w_sub_col_idx in range_(WNITER):
             r = c_row + warp_row * WM + w_sub_row_idx * WSUBM
@@ -794,7 +784,7 @@ def sgemm_warp_tiling[
                                 w_sub_row_idx * TM + res_idx_m,
                                 w_sub_col_idx * TN + res_idx_n + j,
                             ]
-                            + one
+                            + 1
                         )
                     vector.store(
                         tmp,