Some observations on weighted GMRES

We investigate the convergence of the weighted GMRES method for solving linear systems. Two different weighting variants are compared with unweighted GMRES for three model problems, giving a phenomenological explanation of cases where weighting improves convergence, and a case where weighting has no effect on the convergence. We also present new alternative implementations of the weighted Arnoldi algorithm which may be favorable in terms of computational complexity, and examine stability issues connected with these implementations. Two implementations of weighted GMRES are compared for a large number of examples. We find that weighted GMRES may outperform unweighted GMRES for some problems, but more often this method is not competitive with other Krylov subspace methods like GMRES with deflated restarting or BICGSTAB, in particular when a preconditioner is used

Platform-independent Dynamic Reconfiguration of Distributed Applications

The aim of dynamic reconfiguration is to allow a system to evolve incrementally from one configuration to another at run-time, without restarting it or taking it offline. In recent years, support for transparent dynamic reconfiguration has been added to middleware platforms, shifting the complexity required to enable dynamic reconfiguration to the supporting infrastructure. These approaches to dynamic reconfiguration are mostly platform-specific and depend on particular implementation approaches suitable for particular platforms. In this paper, we propose an approach to dynamic reconfiguration of distributed applications that is suitable for application implemented on top of different platforms. This approach supports a platform-independent view of an application that profits from reconfiguration transparency. In this view, requirements on the ability to reconfigure components are expressed in an abstract manner. These requirements are then satisfied by platform-specific realizations