
Worked on the ROCm/rocWMMA repository to deliver low-precision general matrix multiplication (GEMM) capabilities, focusing on both FP8 and int8 data paths. Developed a performance-optimized FP8 GEMM kernel using C++ and the rocWMMA cooperative API, leveraging inter-warp data sharing and pre-fetching techniques to reduce memory latency and improve throughput. Enabled int8 GEMM support by updating type definitions and test infrastructure, broadening the scope of matrix multiply workloads. The work emphasized GEMM optimization, GPU computing, and high-performance computing, aligning with business goals to accelerate inference pipelines and expand hardware utilization for low-precision linear algebra operations.
September 2025 monthly performance summary for ROCm/rocWMMA focusing on delivering low-precision GEMM capabilities and broadening test coverage for matrix multiply workloads. The month centered on implementing high-value kernels and enabling benchmarking for FP8 and int8 data paths, aligning with business goals of accelerating inference pipelines and expanding hardware utilization.
September 2025 monthly performance summary for ROCm/rocWMMA focusing on delivering low-precision GEMM capabilities and broadening test coverage for matrix multiply workloads. The month centered on implementing high-value kernels and enabling benchmarking for FP8 and int8 data paths, aligning with business goals of accelerating inference pipelines and expanding hardware utilization.

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