Parallel Implementation and Performance Comparison of BiCGStab for Massive Sparse Linear System of Equations on GPU Libraries

机译：GPU库上大规模稀疏线性方程组的BiCGStab并行实现和性能比较

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摘要

Biconjugate Gradient Stabilized (BiCGStab) method is a popular iterative method for solving massive linear system of equations with sparse asymmetric coefficient matrix. Implementing methods of BiCGStab on CUDA-enabled GPU libraries are given in the paper. Performance comparisons are made. Different matrix storage formats were used in the implementations and the experiments. Based on the study, suggestions of solving different kinds of linear system of equations are given.

机译：双共轭梯度稳定（BiCGStab）方法是一种用于求解具有稀疏非对称系数矩阵的大规模线性方程组的流行迭代方法。本文给出了在支持CUDA的GPU库上BiCGStab的实现方法。性能比较。在实现和实验中使用了不同的矩阵存储格式。在此基础上，提出了求解不同线性方程组的建议。

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《IEEE international conference on Autonomic and Trusted Computing;IEEE international conference on Ubiquitous Intelligence and Computing;IEEE international conference on Scalable Computing and Communications and Its Associated Workshops;IEEE international conference on cloud and big data computing;IEEE international conference on internet of people》|2015年|603-608|共6页
会议地点 Beijing(CN)
作者
Liankai Yao; Xiaohui Ji; Shuai Liu; Jiayue Yang;
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作者单位

Sch. of Inf. Eng., China Univ. of Geosci. (Beijing), Beijing, China;

Sch. of Inf. Eng., China Univ. of Geosci. (Beijing), Beijing, China;

Sch. of Inf. Eng., China Univ. of Geosci. (Beijing), Beijing, China;

Sch. of Inf. Eng., China Univ. of Geosci. (Beijing), Beijing, China;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Sparse matrices; Libraries; Graphics processing units; Mathematical model; Linear systems; Kernel; Parallel processing;

机译：稀疏矩阵;库;图形处理单元;数学模型;线性系统;内核;并行处理;

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专利

1. A Performance Comparison of the Parallel Preconditioners for Iterative Methods for Large Sparse Linear Systems Arising from Partial Differential Equations on Structured Grids [J] . Sangback MA IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences . 2008,第9期

机译：结构网格上由偏微分方程引起的大型稀疏线性系统迭代方法并行预处理器的性能比较
2. A parallel and vectorial implementation of basic linear algebra subroutines in iterative solving of large sparse linear systems of equations [J] . Magnin H., Coulomb J.L. IEEE Transactions on Magnetics . 1989,第4期

机译：大型稀疏线性方程组的迭代求解中的基本线性代数子例程的并行和矢量实现
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机译：从图块算法到条带算法：高斯方法的GPU上基于CUBLAS的并行实现，用于解析存储在固态设备阵列上的超大型密集线性系统
4. Parallel Implementation and Performance Comparison of BiCGStab for Massive Sparse Linear System of Equations on GPU Libraries [C] . Liankai Yao, Xiaohui Ji, Shuai Liu, IEEE international conference on Autonomic and Trusted Computing . 2015

机译：BICGSTAB对GPU库大规模稀疏线性系统的平行实现及性能比较
5. Solving large sparse systems of nonlinear equations and nonlinear least squares problems using tensor methods on sequential and parallel computers*. [D] . Bouaricha, Ali. 1992

机译：在连续和并行计算机上使用张量法来求解非线性方程和非线性最小二乘问题的大型稀疏系统。
6. Transforming Lindblad Equations into Systems of Real-Valued Linear Equations: Performance Optimization and Parallelization of an Algorithm [O] . Iosif Meyerov, Evgeny Kozinov, Alexey Liniov, 2020

机译：将Lindblad方程转换为实值线性方程的系统：算法的性能优化和并行化
7. The efficiency comparison of solvers for sparse linear algebraic equations systems based on the BiCGStab and FGMRES methods [O] . I. Marchevsky, V. Puzikova 2018

机译：基于BICGSTAB和FGMRES方法的稀疏线性代数方程系统求解器的效率比较
8. Solution of large, sparse systems of linear equations in massively parallel applications [R] . Jones, M T, Plassmann, P E 1992

机译：大规模并行应用中大型稀疏线性方程组的求解

Parallel Implementation and Performance Comparison of BiCGStab for Massive Sparse Linear System of Equations on GPU Libraries

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