diff --git a/media/docs/cpp/dependent_kernel_launch.md b/media/docs/cpp/dependent_kernel_launch.md
index 1beb8bf7..6d41d068 100644
--- a/media/docs/cpp/dependent_kernel_launch.md
+++ b/media/docs/cpp/dependent_kernel_launch.md
@@ -5,11 +5,11 @@ overlap their execution, named
 [Programmatic Dependent Launch (PDL)](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#programmatic-dependent-launch-and-synchronization).
 This allows kernels with conflict in global memory to programmatically and safely overlap portions
 of their execution. Primary kernel can signal it is about to finish execution, and the next kernel is expected to 
-programatically wait on the previous kernel to finish flushing its memory.
+programmatically wait on the previous kernel to finish flushing its memory.
 
 We enable PDL by setting a flag through the extended CUDA launch APIs. All CUTLASS kernels with PDL support
 will wait on the prior kernel to flush its output to memory and signal the next kernel to start. This means
-they can safely be dropped in with any other set of kernels using PDL as long as they also adhear to waiting on
+they can safely be dropped in with any other set of kernels using PDL as long as they also adhere to waiting on
 the prior to flush its memory as well. 
 
 For more information, we refer you to the [PDL section in the CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#programmatic-dependent-launch-and-synchronization).
@@ -36,10 +36,10 @@ gemm.run(
 ## Model-Aware Optimizations with PDL
 
 In [example 63](https://github.com/NVIDIA/cutlass/tree/main/examples/63_hopper_gemm_with_weight_prefetch/README.md), we use PDL to explicitly optimize for 
-performance of kernels where we know that one of the input matricies (our weights) will not be produced by a prior 
+performance of kernels where we know that one of the input matrices (our weights) will not be produced by a prior 
 kernel. In that case, we only need to wait on the prior kernels memory flush in order to load the other input matrix 
 (our activations). During our prologue, we can prefetch our weights to improve performance for memory bandwidth-bound
-problem sizes. For more informations we refer the reader to [the example](https://github.com/NVIDIA/cutlass/tree/main/examples/63_hopper_gemm_with_weight_prefetch/README.md).
+problem sizes. For more information, we refer the reader to [the example](https://github.com/NVIDIA/cutlass/tree/main/examples/63_hopper_gemm_with_weight_prefetch/README.md).
 
 ## Copyright