A fast and robust mixed-precision solver for the solution of sparse symmetric linear systems

A fast and robust mixed-precision solver for the solution of sparse symmetric linear systems
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用于求解稀疏对称线性系统的快速、鲁棒的混合精度求解器

DOI:
10.1145/1731022.1731027
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发表时间:
2010
影响因子:
2.7
通讯作者:
Hogg J
Hogg J
中科院分区:
计算机科学3区
文献类型:
--
作者:
Hogg J

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在许多当前和新兴的计算架构上,单精度计算至少是双精度计算的两倍。此外,使用单精度可以减少对存储器带宽的压力。对于线性系统的解使用单精度的惩罚是计算解的精度的潜在损失。对于稀疏线性系统,使用混合精度(其中双精度迭代方法通过单精度因子分解进行预处理)可以更快地恢复高精度解,并且比使用双精度运算运行的稀疏直接求解器使用更少的内存。在本文中,我们考虑在稀疏对称线性系统的直接求解器中使用单精度,利用存储器需求的减少和性能增益。我们开发了一个实用的算法,应用混合精度的方法,并建议参数和技术,以尽量减少迭代恢复过程所需的解决方案的数量。这些实验为我们的新代码HSL_MA79提供了基础-一个快速,鲁棒,混合精度稀疏对称求解器,包含在数学软件库HSL中。
On many current and emerging computing architectures, single-precision calculations are at least twice as fast as double-precision calculations. In addition, the use of single precision may reduce pressure on memory bandwidth. The penalty for using single precision for the solution of linear systems is a potential loss of accuracy in the computed solutions. For sparse linear systems, the use of mixed precision in which double-precision iterative methods are preconditioned by a single-precision factorization can enable the recovery of high-precision solutions more quickly and use less memory than a sparse direct solver run using double-precision arithmetic.In this article, we consider the use of single precision within direct solvers for sparse symmetric linear systems, exploiting both the reduction in memory requirements and the performance gains. We develop a practical algorithm to apply a mixed-precision approach and suggest parameters and techniques to minimize the number of solves required by the iterative recovery process. These experiments provide the basis for our new code HSL_MA79—a fast, robust, mixed-precision sparse symmetric solver that is included in the mathematical software library HSL.Numerical results for a wide range of problems from practical applications are presented.
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发表时间: 2009-02
期刊: ACM Trans. Math. Softw.
影响因子: --
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发表时间: 2005-06
期刊: SIAM J. Matrix Anal. Appl.
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