基于GPU—CUDA的共轭斜量法实现及性能对比
量法是求解一般性大型线性方程组快速而非常有效的方法。关键词： GPU； CUDA； 大型线性方程组； 共轭斜量法； 算法； 并行计算中图分类号：TP311.1 文献标志码：A 文章编号：1006-8228（2014）04-04-03Abstract： The numerical solution for partial differential equations （including finite difference method， and finite element method） and a large number of equations of mathematical physics problems will eventually evolve into solving a large-scale linear equation system. Therefore， studying fast， stable and accurate solutions for large-scale linear equation systems has been a hot topic in the field of numerical calculation for years， which has special significance. Among iterative methods， conjugate gradient method is recognized as one of the best methods. However， this method is only applicable to linear equation systems in which coefficient matrix is symmetric and positive definite. Besides， in conventional CPU implementation， the method for solving a large-scale linear equation system is time-consuming. After the linear equations coefficient matrix A is converted into a symmetric matrix by， the fast c
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