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Algorithm 881: A Set of Flexible GMRES Routines for Real and Complex Arithmetics on High-Performance Computers

机译:算法881:高性能计算机上用于实数和复数算法的一组灵活GMRES例程

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In this article we describe our implementations of the FGMRES algorithm for both real and complex, single and double precision arithmetics suitable for serial, shared-memory, and distributed-memory computers. For the sake of portability, simplicity, flexibility, and efficiency, the FGMRES solvers have been implemented in Fortran 77 using the reverse communication mechanism for the matrix-vector product, the preconditioning, and the dot-product computations. For distributed-memory computation, several orthogonalization procedures have been implemented to reduce the cost of the dot-product calculation, which is a well-known bottleneck of efficiency for Krylov methods. Furthermore, either implicit or explicit calculation of the residual at restart is possible depending on the actual cost of the matrix-vector product. Finally, the implemented stopping criterion is based on a normwise backward error.
机译:在本文中,我们描述了适用于串行,共享内存和分布式内存计算机的实数和复数,单精度和双精度算法的FGMRES算法实现。为了便于携带,简单,灵活和高效,已在Fortran 77中使用针对矩阵矢量乘积,预处理和点积计算的反向通信机制实现了FGMRES求解器。对于分布式内存计算,已实施了几种正交化程序以降低点积计算的成本,这是Krylov方法效率的众所周知的瓶颈。此外,根据矩阵向量乘积的实际成本,可以在重新启动时隐式或显式计算残差。最后,实施的停止标准基于规范后向误差。

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