diff --git a/examples/grouped_gemm.py b/examples/grouped_gemm.py
index d6d51b597..1a57d9f55 100644
--- a/examples/grouped_gemm.py
+++ b/examples/grouped_gemm.py
@@ -165,9 +165,9 @@ def grouped_gemm_jagged_persistent(
 
             if m_size > 0:
                 # Compute tile grid dimensions for current group
-                num_m_tiles = (m_size + BLOCK_M - 1) // BLOCK_M
+                num_m_tiles = helion.cdiv(m_size, BLOCK_M)  # pyright: ignore[reportArgumentType]
                 # Calculate number of N tiles (shared across all groups)
-                num_n_tiles = (N + BLOCK_N - 1) // BLOCK_N
+                num_n_tiles = helion.cdiv(N, BLOCK_N)  # pyright: ignore[reportArgumentType]
                 num_group_tiles = num_m_tiles * num_n_tiles
 
                 # Distribute tiles among workers using strided access pattern
diff --git a/examples/rms_norm.py b/examples/rms_norm.py
index 0e2c28d25..bf44214aa 100644
--- a/examples/rms_norm.py
+++ b/examples/rms_norm.py
@@ -96,7 +96,7 @@ def rms_norm_bwd(
     m_block = hl.register_block_size(x.size(0))
     grad_x = torch.empty_like(x)
     grad_weight = x.new_empty(
-        [(x.size(0) + m_block - 1) // m_block, *weight.shape], dtype=torch.float32
+        [helion.cdiv(x.size(0), m_block), *weight.shape], dtype=torch.float32
     )
     weight_shape = hl.specialize(weight.size(0))
     for mb_cta in hl.tile(x.size(0), block_size=m_block):
diff --git a/test/test_examples.expected b/test/test_examples.expected
index 5e56dbe53..4610a115c 100644
--- a/test/test_examples.expected
+++ b/test/test_examples.expected
@@ -3153,6 +3153,7 @@ def moe_matmul_ogs(A: torch.Tensor, W: torch.Tensor, expert_token_counts: torch.
 from __future__ import annotations
 
 import torch
+import helion
 import triton
 import triton.language as tl
 from helion.runtime import default_launcher as _default_launcher
@@ -3218,7 +3219,7 @@ def rms_norm_bwd(grad_out: torch.Tensor, x: torch.Tensor, weight: torch.Tensor,
     """
     m_block = 32
     grad_x = torch.empty_like(x)
-    grad_weight = x.new_empty([(x.size(0) + m_block - 1) // m_block, *weight.shape], dtype=torch.float32)
+    grad_weight = x.new_empty([helion.cdiv(x.size(0), m_block), *weight.shape], dtype=torch.float32)
     _BLOCK_SIZE_0 = 32
     _RDIM_SIZE_2 = 64
     _launcher(_helion_rms_norm_bwd, (triton.cdiv(x.size(0), _BLOCK_SIZE_0),), x, grad_out, rsqrt, weight, grad_x, grad_weight, x.size(0), grad_out.stride(0), grad_out.stride(1), grad_weight.stride(0), grad_weight.stride(1), grad_x.stride(0), grad_x.stride(1), rsqrt.stride(0), weight.stride(0), x.stride(0), x.stride(1), _BLOCK_SIZE_0, _RDIM_SIZE_2, num_warps=4, num_stages=3)