Solvers for systems of large sparse linear and nonlinear equations based on multi-GPUs

被引:0
|
作者
Liu, Sha [1 ]
Zhong, Chengwen [1 ,2 ]
Chen, Xiaopeng [3 ]
机构
[1] National Key Laboratory of Science and Technology on Aerodynamic Design and Research, Northwestern Polytechnical University, Xi'an, 710072, China
[2] Center for High Performance Computing, Northwestern Polytechnical University, Xi'an, 710072, China
[3] School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi'an, 710072, China
关键词
Numerical methods - Conjugate gradient method - Digital storage - Newton-Raphson method - Digital devices - Program processors;
D O I
暂无
中图分类号
学科分类号
摘要
Numerical treatment of engineering application problems often eventually results in a solution of systems of linear or nonlinear equations. The solution process using digital computational devices usually takes tremendous time due to the extremely large size encountered in most real-world engineering applications. So, practical solvers for systems of linear and nonlinear equations based on multi graphic process units (GPUs) are proposed in order to accelerate the solving process. In the linear and nonlinear solvers, the preconditioned bi-conjugate gradient stable (PBi-CGstab) method and the Inexact Newton method are used to achieve the fast and stable convergence behavior. Multi-GPUs are utilized to obtain more data storage that large size problems need.
引用
收藏
页码:300 / 308
相关论文
共 50 条
  • [21] An efficient multi-algorithms sparse linear solver for GPUs
    Jost, Thomas
    Contassot-Vivier, Sylvain
    Vialle, Stephane
    PARALLEL COMPUTING: FROM MULTICORES AND GPU'S TO PETASCALE, 2010, 19 : 546 - 553
  • [22] Automatic Selection of Sparse Triangular Linear System Solvers on GPUs through Machine Learning Techniques
    Dufrechou, Ernesto
    Ezzatti, Pablo
    Quintana-Orti, Enrique S.
    2019 31ST INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE AND HIGH PERFORMANCE COMPUTING (SBAC-PAD 2019), 2019, : 41 - 47
  • [23] ZMCintegral-v5: Support for Integrations with the scanning of large parameter space on multi-GPUs
    Zhang, Jun-Jie
    Wu, Hong-Zhong
    COMPUTER PHYSICS COMMUNICATIONS, 2020, 251
  • [24] Solving lattice QCD systems of equations using mixed precision solvers on GPUs
    Clark, M. A.
    Babich, R.
    Barros, K.
    Brower, R. C.
    Rebbi, C.
    COMPUTER PHYSICS COMMUNICATIONS, 2010, 181 (09) : 1517 - 1528
  • [25] A wavelet approach for the construction of multi-grid solvers for large linear systems
    Keller, W
    VISTAS FOR GEODESY IN THE NEW MILLENNIUM, 2002, 125 : 265 - 270
  • [26] INEXACT TRUST REGION METHOD FOR LARGE SPARSE SYSTEMS OF NONLINEAR EQUATIONS
    LUKSAN, L
    JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 1994, 81 (03) : 569 - 590
  • [27] Inexact Newton method for solving large and sparse systems of nonlinear equations
    杨凤红
    唐云
    何淼
    延边大学学报(自然科学版), 2003, (03) : 157 - 160
  • [28] Parallel solution of very large sparse systems of linear algebraic equations
    Zlatev, Z
    LARGE-SCALE SCIENTIFIC COMPUTING, 2003, 2907 : 53 - 64
  • [29] NOTE ON SOLUTION OF LARGE SPARSE SYSTEMS OF NON-LINEAR EQUATIONS
    TEWARSON, RP
    STEPHENSON, JL
    JUANG, LL
    JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS, 1978, 63 (02) : 439 - 445
  • [30] A Fuzzy Neural Network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations
    Zhang, Chao-Long
    Xu, Yuan-Ping
    Xu, Zhi-Jie
    He, Jia
    Wang, Jing
    Adu, Jian-Hua
    INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING, 2018, 15 (02) : 181 - 193