GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation

被引：23

作者：

Peng, Shaoyi ^{[1
]}

Tan, Sheldon X. -D. ^{[2
]}

机构：

[1] Univ Calif Riverside, Dept Elect & Comp Engn, Riverside, CA 92521 USA

[2] Univ Calif Riverside, UCR, Dept Elect Engn, Riverside, CA USA

来源：

IEEE DESIGN & TEST | 2020年 / 37卷 / 03期

关键词：

Sparse matrices; Graphics processing units; Parallel processing; Circuit simulation; Kernel; Instruction sets; Task analysis; GPU; LU factorization; left-looking LU factorization; sparse matrices; GLU;

D O I：

10.1109/MDAT.2020.2974910

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Editor's note: Many scientific computing problems, including circuit simulations, rely on efficient lower-upper (LU) decomposition of sparse matrices. Prior studies took advantage of GPUs to parallelize LU decomposition, but they suffer from nontrivial data dependencies. This article presents a new method, called GLU3.0, to accelerate GPU-based sparse LU factorization. -Umit Ogras, Arizona State University

引用

页码：78 / 90

页数：13

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