GPU-Based Parallel Nonlinear Conjugate Gradient Algorithms

被引：0
|
作者：
Galiano, V. [1 ]
Migallon, H. [1 ]
Migallon, V. [2 ]
Penades, J. [2 ]
机构：
[1] Univ Miguel Hernandez, Dept Phys & Comp Architectures, Alicante, Spain
[2] Univ Alicante, Dept Comp Sci & Artificial Intelligence, Alacant, Spain
来源：
PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, GRID AND CLOUD COMPUTING FOR ENGINEERING | 2011年 / 95卷
关键词：
GPGPU; GPU libraries; multicore; nonlinear conjugate gradient algorithms; parallel preconditioners; ILU factorizations; two-stage methods; Bratu problem;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper we describe some parallel algorithms for solving nonlinear systems using CUDA (Compute Unified Device Architecture) over a GPU (Graphics Processing Unit). The proposed algorithms are based on both the Fletcher-Reeves version of the nonlinear conjugate gradient method and a polynomial preconditioner type based on block two-stage methods. Several strategies of parallelization and different storage formats for sparse matrices are discussed. The reported numerical experiments analyze the behavior of these algorithms working in a fine grain parallel environment compared with a thread-based environment.
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页数：17
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