Benchmarking GPU Matrix Operations Optimizations
Completed project for CS6501: GPU Architectures at the University of Virginia. This research presents a comprehensive benchmarking study of matrix operation optimizations across NVIDIA GPU architectures, focusing on matrix transpose and multiplication. Through systematic evaluation of custom CUDA kernels and library implementations across RTX 2080 Ti and A100 GPUs, demonstrated that vectorized implementations achieve up to 6x speedup over naive approaches, reaching 1800 GB/s throughput on A100.