
Worked on ROCm/pytorch and pytorch/pytorch repositories to enhance CUDA kernel debugging and reliability. Developed the CUDA_KERNEL_ASSERT_PRINTF helper, which integrates printf-style diagnostics with assertions to provide device-side context in error messages, reducing the need for recompilation and reruns during kernel debugging. Used C++ and CUDA to ensure performance sensitivity by gating printf calls in critical paths. Additionally, improved error reporting and index bounds validation for the vectorized gather kernel by reinstating format-string arguments in CUDA_KERNEL_ASSERT_VERBOSE, supporting robust debugging and validation. Demonstrated skills in CUDA programming, debugging, and performance optimization while maintaining traceability through thorough testing and validation.
Month 2025-12: Stabilized the vectorized gather path in pytorch/pytorch by fixing error reporting and index bounds validation. Reinstated missing format-string arguments in CUDA_KERNEL_ASSERT_VERBOSE (IndexKernelUtils.cu) to improve debugging for vectorized gather kernels, aligned with PR #170913 and D89575112. Executed sanity checks to prevent grid-config regressions and validated results across CUDA kernels and CPU.
Month 2025-12: Stabilized the vectorized gather path in pytorch/pytorch by fixing error reporting and index bounds validation. Reinstated missing format-string arguments in CUDA_KERNEL_ASSERT_VERBOSE (IndexKernelUtils.cu) to improve debugging for vectorized gather kernels, aligned with PR #170913 and D89575112. Executed sanity checks to prevent grid-config regressions and validated results across CUDA kernels and CPU.
September 2025: Delivered a new CUDA_KERNEL_ASSERT_PRINTF helper for CUDA kernel debugging in ROCm/pytorch. This feature combines printf-style diagnostics with assertions to provide device-side context in error messages, improving developer experience by reducing the need to recompile and re-run workflows. The changes maintain performance sensitivity by avoiding printf calls in critical paths and complement the existing CUDA_KERNEL_ASSERT_MSG macro, enabling richer, faster-to-diagnose kernel failures.
September 2025: Delivered a new CUDA_KERNEL_ASSERT_PRINTF helper for CUDA kernel debugging in ROCm/pytorch. This feature combines printf-style diagnostics with assertions to provide device-side context in error messages, improving developer experience by reducing the need to recompile and re-run workflows. The changes maintain performance sensitivity by avoiding printf calls in critical paths and complement the existing CUDA_KERNEL_ASSERT_MSG macro, enabling richer, faster-to-diagnose kernel failures.

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