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Structured adaptive mesh refinement (SAMR) techniques provide an effective means for dynamically concentrating computational effort and resources to appropriate regions in the application domain. However, due to their dynamism and space-time heterogeneity, scalable parallel implementation of SAMR applications remains a challenge. This paper investigates hybrid runtime management strategies and presents an adaptive hierarchical multi-partitioner (AHMP) framework. AHMP dynamically applies multiple partitioners to different regions of the domain, in a hierarchical manner, to match the local requirements of the regions. Key components of the AHMP framework include a segmentation-based clustering algorithm (SBC) that can efficiently identify regions in the domain with relatively homogeneous partitioning requirements, mechanisms for characterizing the partitioning requirements of these regions, and a runtime system for selecting, configuring and applying the most appropriate partitioner to each region. Further, to address dynamic resource situations for long running applications, AHMP provides a hybrid partitioning strategy (HPS), which involves application-level pipelining, trading space for time when resources are sufficiently large and under-utilized, and an application-level out-of-core strategy (ALOC), trading time for space when resources are scarce in order to enhance the survivability of applications. The AHMP framework has been implemented and experimentally evaluated on up to 1280 processors of the IBM SP4 cluster at San Diego Supercomputer Center.
Parallel Computing, Structured Adaptive Mesh Refinement, Dynamic Load Balancing, Hierarchical Multi-Partitioner, High Performance Computing
Manish Parashar, Xiaolin Li, "Hybrid Runtime Management of Space-Time Heterogeneity for Parallel Structured Adaptive Applications", IEEE Transactions on Parallel & Distributed Systems, vol. 18, no. , pp. 1202-1214, September 2007, doi:10.1109/TPDS.2007.1038
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