I work on GPU runtime reliability and performance, focusing on CUDA resource lifecycles, distributed failure diagnosis, and reproducible validation. I contribute fixes and regression tests to PyTorch and vLLM.
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PyTorch: Fixed CUDA SymmetricMemory resource leaks involving exported descriptors and peer pointer tables. #200021 · #200738
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vLLM: Removed unnecessary index computation from native per-token FP8 quantization. #59800
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vLLM: Added validation for encoder-cache hits with mismatched embedding counts. #57696
vLLM Runtime Reliability Lab investigates failures where processes remain alive but useful work stops, using fault injection, external observations, and base/fix comparisons.
Published cases include independent validation of a vLLM stall where
/health stayed green while token progress stopped, and a PyTorch
investigation showing why a live rank could disappear from Flight Recorder
dumps during legacy NCCL teardown.
The proposed legacy fix, #197232, was closed without merging as the maintainer preferred to focus on nccl2; the reproduced behavior remains documented in #196968.
A GPU-free replay checks the archived vLLM experiment evidence without rerunning the workload.
Start with a concrete failure, reduce it to a specific mechanism, and check the fix against a failing baseline. Keep measured results, source-level conclusions, and unverified hypotheses separate.