Publications

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Structured adaptive mesh refinement methods are being widely used for computer simulations of various physical phenomena. Parallel implementations potentially offer realistic simulations of complex three-dimensional applications. But achieving good scalability for large-scale applications is non-trivial. Performance is limited by the partitioner's ability to efficiently use the underlying parallel computer's resources. Designed on sound SAMR principles, Nature+Fable is a hybrid, dedicated SAMR partitioning tool that brings together the advantages of both domain-based and patch-based techniques while avoiding their drawbacks. But the original bi-level partitioning approach in Nature+Fable is insufficient as it for realistic applications regards frequently occurring bi-levels as 'impossible' and fails. This document describes an improved bi-level partitioning algorithm that successfully copes with all possible hi-levels. The improved algorithm uses the original approach side-by-side with a new, complementing approach. By using a new, customized classification method, the improved algorithm switches automatically between the two approaches. This document describes the algorithms, discusses implementation issues, and presents experimental results. The improved version of Nature+Fable was found to be able to handle realistic applications and also to generate less imbalances, similar box count, but more communication as compared to the native, domain-based partitioner in the SAMR framework AMROC.

Achieving good scalability for large simulations based on structured adaptive mesh refinement is non-trivial. Performance is limited by the partitioner's ability to efficiently use the underlying parallel computer's resources. Domainbased partitioners serve as a foundation for techniques designed to improve the scalability and they have traditionally been designed on the basis of an independence assumption regarding the computational flow among grid patches at different refinement levels. But this assumption does not hold in practice. Hence the effectiveness of these techniques is significantly impaired. This paper introduces a partitioning method designed on the true premises. The method is tested for four different applications exhibiting different behaviors. The results show that synchronization costs on average can he reduced by 75 percent. The conclusion is that the method is suitable as a foundation in general hierarchical methods designed to improve the scalability of structured adaptive mesh refinement applications.

Structured adaptive mesh refinement (SAMR) methods are being widely used for computer simulations of various physical phenomena. Parallel implementations potentially offer realistic simulations of complex, three-dimensional applications. But achieving good scalability for large-scale applications is non-trivial. Performance is limited by the partitioners ability to efficiently use the underlying computer's resources. The goal of our research project is to improve scalability for general SAMR applications executing on general parallel computers. We engineer the dynamically adaptive meta-partitioner, able to select and configure the most appropriate partitioning method at run-time, based on system and application state. This presentation gives an overview of our project, reports on recent achievements, and discusses the project's significance in a wider scientific context.

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Significantly improving the scalability of large structured adaptive mesh refinement (SAMR) applications is challenging. It requires sophisticated capabilities for using the underlying parallel computer's resources in the most efficient way. This is non-trivial, since the basic conditions for how to allocate the resources change dramatically during run-time due to the dynamics inherent in these applications. This paper presents a first characterization of a hybrid and dynamic partitioner for parallel SAMR applications. Specifically, we investigate parameter settings for trade-offs like communication vs. load balance and speed vs. quality. The key contribution is that the characterization shows that the partitioner is able to respond accurately to stimuli from system and application state, and hence adapt to various SAMR scenarios. This potentially reduces the run-time for large SAMR applications.