Dense Matrix Multiplication

Dense matrix multiplication (GEMM) is a fundamental operation in many scientific computing and machine learning applications, with a primary objective of optimizing its speed and accuracy. Current research focuses on improving GEMM performance through techniques like low-rank approximations for low-precision computations, auto-tuning for sparse matrices (SpMM and SDDMM), and specialized hardware architectures tailored to specific matrix structures and dataflows (e.g., row-stationary algorithms for graph convolutional networks). These advancements are crucial for accelerating large-scale computations in fields like deep learning and graph neural networks, leading to faster training times and improved energy efficiency in various applications.