
Worked on performance-focused enhancements to matrix multiplication and kernel scheduling in the facebookexperimental/triton and pytorch/pytorch repositories. Developed features such as budget-aware warp allocation, memory usage optimizations for addmm broadcasts, and TMA descriptor support for fused pointwise epilogues, enabling more efficient GPU utilization and broader configuration support. Implemented multi-CTA MMA support with per-MMA cross-CTA barriers and dependency checks to improve GEMM throughput and correctness. Leveraged CUDA, Python, and MLIR to optimize kernel performance, ensure robust scheduling, and maintain code quality. The work emphasized scalable GPU programming and contributed to more efficient deep learning operations across both repositories.
June 2026: Delivered performance-focused enhancements to GEMM in facebookexperimental/triton by enabling multi-CTA MMAs with per-MMA cross-CTA barriers and adding a dependency check to prevent unsupported multi-MMA configurations. These changes improve potential GEMM throughput for 2-CTA scenarios while safeguarding correctness by rejecting problematic data dependencies. The work strengthens Triton kernel scheduling and contributes to more robust, scalable matrix multiplication on multi-CTA architectures.
June 2026: Delivered performance-focused enhancements to GEMM in facebookexperimental/triton by enabling multi-CTA MMAs with per-MMA cross-CTA barriers and adding a dependency check to prevent unsupported multi-MMA configurations. These changes improve potential GEMM throughput for 2-CTA scenarios while safeguarding correctness by rejecting problematic data dependencies. The work strengthens Triton kernel scheduling and contributes to more robust, scalable matrix multiplication on multi-CTA architectures.
May 2026 performance-focused monthly summary across facebookexperimental/triton and pytorch/pytorch. Focused on business value and technical achievements in kernel scheduling, memory efficiency, and Inductor/Triton integration. Highlights include budget-aware warp allocation and specialization, memory-usage optimizations enabling more configurations, and end-to-end TMA descriptor support for fused pointwise epilogues in Triton kernels.
May 2026 performance-focused monthly summary across facebookexperimental/triton and pytorch/pytorch. Focused on business value and technical achievements in kernel scheduling, memory efficiency, and Inductor/Triton integration. Highlights include budget-aware warp allocation and specialization, memory-usage optimizations enabling more configurations, and end-to-end TMA descriptor support for fused pointwise epilogues in Triton kernels.

Overview of all repositories you've contributed to across your timeline