The MueLu tutorial is written as a hands-on tutorial for MueLu, the next generation multigrid framework in Trilinos. It covers the whole spectrum from absolute beginners’ topics to expert level. Since the focus of this tutorial is on practical and technical aspects of multigrid methods in general and MueLu in particular, the reader is expected to have a basic understanding of multigrid methods and its general underlying concepts. Please refer to multigrid textbooks (e.g. [1]) for the theoretical background.
This is the official user guide for the M UE L U multigrid library in Trilinos version 11.12. This guide provides an overview of M UE L U , its capabilities, and instructions for new users who want to start using M UE L U with a minimum of effort. Detailed information is given on how to drive M UE L U through its XML interface. Links to more advanced use cases are given. This guide gives information on how to achieve good parallel performance, as well as how to introduce new algorithms. Finally, readers will find a comprehensive listing of available M UE L U options. Any options not documented in this manual should be considered strictly experimental.
We explore rearrangements of classical uncertainty quantification methods with the aim of achieving higher aggregate performance for uncertainty quantification calculations on emerging multicore and many core architectures. We show a rearrangement of the stochastic Galerkin method leads to improved performance and scalability on several computational architectures whereby uncertainty information is propagated at the lowest levels of the simulation code improving memory access patterns, exposing new dimensions of fine grained parallelism, and reducing communication. We also develop a general framework for implementing such rearrangements for a diverse set of uncertainty quantification algorithms as well as computational simulation codes to which they are applied.