Optimizing Matrix Multiplication on RDNA3

The article discusses the optimization of FP32 matrix multiplication on AMD's RDNA3 GPUs, specifically targeting performance improvements over the existing rocBLAS library. The author outlines a series of iterative optimizations implemented across eight different kernels, focusing on 4096x4096 matrix sizes for simplicity. The initial naive implementation achieved only 1010.60 GFLOPS, significantly below the theoretical performance of 61.44 TFLOPS. The author then compares this with the rocBLAS implementation, which performed at 30,547 GFLOPS. The key issue identified was inefficient global memory access, leading to high latency. To address this, the author introduced Local Data Store (LDS) tiling, which caches data in faster local memory, improving performance to 4017.99 GFLOPS. Despite these improvements, the performance still lagged behind the theoretical maximum due to underutilization of the GPU's capabilities. The article emphasizes the importance of optimizing memory access patterns and leveraging the architecture of RDNA3 to achieve better performance in matrix multiplication, a critical operation in machine learning applications.

- The optimization of matrix multiplication on AMD RDNA3 GPUs can significantly outperform existing libraries like rocBLAS.

- Initial naive implementations yield low performance, highlighting the need for efficient memory access strategies.

- Utilizing Local Data Store (LDS) tiling can improve performance by reducing memory access latency.

- Despite optimizations, achieving theoretical performance limits remains challenging due to underutilization of GPU resources.