计算机工程与科学2025，Vol.47Issue(9)：1544-1554,11.DOI:10.3969/j.issn.1007-130X.2025.09.003
MIMD众核架构ILU分解并行算法优化研究
Optimization of ILU decomposition parallel algorithm on MIMD many-core architecture
摘要
Abstract
ILU(Incomplete LU)factorization is widely used in solving large-scale sparse linear sys-tems.It can effectively reduce the number of iterations and improve solving efficiency.However,due to the data dependence of linear systems and the irregularity of computation and memory access during the decomposition process,it is difficult to perform efficient parallel optimization.In the multiple instruc-tion multiple data(MIMD)many-core architecture,numerous parallel computing threads can execute different instructions,which has a natural adaptability to algorithms with irregular control flow.This paper conducts research on the parallel algorithm optimization of ILU factorization on the MIMD many-core architecture PEZY-SC3s processor,proposes an ILU parallel algorithm for the MIMD architecture,and adopts measures such as graph coloring-based parallelism optimization,vector unit-based memory access optimization,thread grouping-based load balancing optimization,and on-chip local storage-based data locality optimization to optimize the algorithm performance.Experimental results show that the proposed ILU parallel factorization algorithm achieves an average speedup of 16.70 and 1.39 compared with the MKL implementation on Intel® Xeon® 4314 CPU and the cuSPARSE implementation on NVIDIA® A30 GPU,respectively.关键词
ILU分解/MIMD众核架构/并行计算Key words
incomplete LU factorization/MIMD many-core architecture/parallel computing分类
信息技术与安全科学引用本文复制引用
石永振,莫淏天,胡星宇,刘杰,王庆林..MIMD众核架构ILU分解并行算法优化研究[J].计算机工程与科学,2025,47(9):1544-1554,11.基金项目
国家重点研发计划(2023YFA1011704,2021YFBO300101) （2023YFA1011704,2021YFBO300101）
