Efficient Partitioning of Dynamic Structured Grid Hierarchies

Abstract: This thesis aims at decreasing execution time for large-scale structured adaptive mesh refinement (SAMR) applications executing on general parallel computers. The key contributions are (1) A conceptual dynamically adaptive meta-partitioner able to select and configure an appropriate partitioning technique, based on application and computer state, and (2) A characterization of new and existing domain-based partitioners, enabling a mapping from application and computer state onto appropriate partitioners, and (3) Sketched scalable solutions, expressed in terms of natural regions and expert algorithms, for the problem to efficiently partition large-scale SAMR applications with deep grid hierarchies executing on general parallel computers, and (4) A software partitioning tool Nature+Fable, implementing the greater parts of these sketched scalable solutions and engineered as part of the meta-partitioner.Both in academia and industry, computer simulations of physical phenomena are becoming increasingly popular as they constitute an important complement to real-life testing. In many cases, such simulations are based on solving partial differential equations by numerical methods. Adaptive methods are crucial to efficiently utilize computer resources such as memory and CPU. But even with adaption, the simulations are computationally demanding and yield huge data sets. Thus, parallelization is a necessity, demanding the next level of wise resource utilization --- the partitioning of data. Adaption causes the workload to change dynamically, calling for dynamic (re-) partitioning to maintain efficient resource utilization.The primary motivation for the present work is twofold, viz. (1) No single partitioning technique performs the best for all applications and computer systems, and (2) No established partitioning technique copes efficiently with large-scale SAMR applications with deep grid hierarchies executing on general parallel computers.The conclusions are that the execution time for large-scale SAMR applications can be decreased by the meta-partitioner, and that our proposed scalable solutions exhibit promising properties and behave as expected or better. Consequently, this thesis takes a step towards decreasing the execution times for large-scale SAMR applications.