Dynamic Scheduling Strategy for Block Parallel Cholesky Factorization Based on Activity on Edge Network

The efficient development of system software and design applications in parallel architecture is a notable challenge considering various aspects, such as load balancing, memory spaces, communication, and synchronization. This paper presents a block parallel Cholesky factorization algorithm for a multicore system, which is developed based on activity on edge network. First, the basic block computing tasks and their dependencies are taken as vertices and edges, respectively, and a directed acyclic graph corresponding to the specific block parallel Cholesky factorization is generated. Next, each edge of the directed acyclic graph is assigned to a weight equal to the processing time of the initial vertex of the edge, and the directed acyclic graph becomes an activity on edge network with only one starting and one ending vertex. Finally, a queuing algorithm is designed for the basic block computing tasks according to the edge activity on edge network, and a dynamic scheduling strategy is developed for block parallel Cholesky factorization. The results of the experiments concerning the parallel execution time of the algorithm in multicore systems with different configurations demonstrate that the proposed algorithm has notable advantages compared with the traditional static scheduling algorithm, and it exhibits satisfactory load balancing, parallelism, and scalability capacities.