关键词：算法；分布式数据处理;；软件工程
摘 要：Matrix multiplication is one of the most fundamental algorithmic problems in numerical linear algebra, distributed computing, scientific computing, and high-performance computing. Parallelization of matrix multiplication has been extensively studied (e.g., 21, 12, 24, 2, 51, 39, 36, 23, 45, 61). It has been addressed using many theoretical approaches, algorithmic tools, and software engineering methods in order to optimize performance and obtain faster and more efficient parallel algorithms and implementations. To design efficient parallel algorithms, it is necessary not only to load balance the computation, but also to minimize the time spent communicating between processors. The interprocessor communication costs are in many cases significantly higher than the computational costs. Moreover, hardware trends predict that more problems will become communication-bound in the future 38, 35. Even matrix multiplication, which is widely considered to be computation-bound, becomes communication-bound when a given problem is run on sufficiently many processors.