Research on distributed coding computation based on matrix multiplication
Zhang Lin · Applied and Computational Engineering · 2023
Matrix multiplication is used in machine learning to train larger, more accurate models with massive data more effectively. The distributed machine learning paradigm necessitates the distributed computing of many matrix multiplications once it is adopted. The execution time of distributed machine learning algorithms increases on dropout nodes, which are computational nodes in distributed clusters that randomly slow down computation due to a variety of factors (e.g., node failure, system failure, communication bottlenecks, etc.), becoming a significant bottleneck in distributed computing systems. It has been discovered that coded computation is less expensive than replica methods and can more effectively reduce the effects of dropped nodes. In this study, we present theoretical insights on how encoded solutions might produce significant gains over unencoded solutions.