In Situ Storage Layout Optimization for AMR Spatio-temporal Read Accesses
| Autor: | Bin Dong, Dharshi Devendran, Xiaocheng Zou, Kesheng Wu, Houjun Tang, Scott Klasky, Steve Harenberg, Daniel F. Martin, Nagiza F. Samatova, Wenzhao Zhang, Suren Byna, David Trebotich |
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| Rok vydání: | 2016 |
| Předmět: |
Scheme (programming language)
Computer science Adaptive mesh refinement 010103 numerical & computational mathematics Parallel computing Data structure 01 natural sciences Data modeling 010101 applied mathematics Set (abstract data type) 0101 mathematics Focus (optics) Throughput (business) computer computer.programming_language |
| Zdroj: | ICPP |
| DOI: | 10.1109/icpp.2016.53 |
| Popis: | Analyses of large simulation data often concentrate on regions in space and in time that contain important information. As simulations adopt Adaptive Mesh Refinement (AMR), the data records from a region of interest could be widely scattered on storage devices and accessing interesting regions results in significantly reduced I/O performance. In this work, we study the organization of block-structured AMR data on storage to improve performance of spatio-temporal data accesses. AMR has a complex hierarchical multi-resolution data structure that does not fit easily with the existing approaches that focus on uniform mesh data. To enable efficient AMR read accesses, we develop an in situ data layout optimization framework. Our framework automatically selects from a set of candidate layouts based on a performance model, and reorganizes the data before writing to storage. We evaluate this framework with three AMR datasets and access patterns derived from scientific applications. Our performance model is able to identify the best layout scheme and yields up to a 3X read performance improvement compared to the original layout. Though it is not possible to turn all read accesses into contiguous reads, we are able to achieve 90% of contiguous read throughput with the optimized layouts on average. |
| Databáze: | OpenAIRE |
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