Matrix Multiplication Kernel

Matrix multiplication kernels are fundamental computational building blocks for numerous scientific and engineering applications, particularly in machine learning and high-performance computing. Current research focuses on optimizing these kernels for specific hardware architectures (GPUs, CPUs, specialized accelerators) and data types (e.g., quantized representations), addressing challenges like memory access patterns, bank conflicts, and communication overhead in both shared and distributed memory environments. These optimizations aim to improve speed, energy efficiency, and numerical accuracy, impacting diverse fields from large language model inference to graph neural network training and scientific simulations. Significant advancements are being made through algorithmic innovations, such as fused operations and novel data structures, alongside hardware-aware kernel design.