add lucas

Signed-off-by: youkaichao <youkaichao@gmail.com>

Author: youkaichao

_posts/2025-11-27-improved-cuda-debugging.md

@@ -317,10 +317,10 @@ With the approach outlined above, we can uncover the full inline chain of the so
 
 ## Conclusion
 
-This blog post introduced two advanced debugging techniques for CUDA kernels. The first technique uses user-triggered core dumps to identify hanging kernels, while the second traces complex kernels back to their source code by leveraging line information embedded in the compiled binary. These techniques are powerful tools for debugging complex issues in CUDA kernels, especially illegal memory access problems.
+This blog post introduced two advanced debugging techniques for CUDA kernels. The first technique uses user-triggered core dumps to identify hanging kernels, while the second traces complex kernels back to their source code by leveraging line information embedded in the compiled binary. These techniques are powerful tools for debugging complex issues in CUDA kernels, especially illegal memory access problems. Using both in tandem we were able to recently debug [a hard-to-reproduce and tricky hang in the CUTLASS MLA attention backend](https://github.com/vllm-project/vllm/pull/26026), which actually stemmed from the upstream CUTLASS code example and has since been fixed in [v4.3.0](https://github.com/NVIDIA/cutlass/commit/b1d6e2c9b334dfa811e4183dfbd02419249e4b52).
 
 The vLLM project aims to provide easy, fast, and affordable LLM serving for everyone, and accessible debugging is an important aspect of this mission. We will continue to share more debugging tips and techniques in the future to build a strong LLM inference ecosystem together. To share your story or usage with vLLM, please submit a PR at [the blogpost repository](https://github.com/vllm-project/vllm-project.github.io).
 
 # Acknowledgement
 
-We would like to thank Ze Long and Sandarbh Jain from NVIDIA for their helpful discussions. Chao Hong from Moonshot AI helped provide the motivating example.
+We would like to thank Ze Long and Sandarbh Jain from NVIDIA for their helpful discussions. Chao Hong from Moonshot AI helped provide the motivating example. Lucas Wilkinson from Red Hat helped polishing the draft.