Name
Abstract | ||
|---|---|---|
Particle data are used across a diverse set of large scale simulations, for example, in cosmology, molecular dynamics and combustion. At scale these applications generate tremendous amounts of data, which is often saved in an unstructured format that does not preserve spatial locality; resulting in poor read performance for post-processing analysis and visualization tasks, which typically make spatial queries. In this work, we explore some of the challenges of large scale particle data management, and introduce new techniques to perform scalable, spatially-aware write and read operations. We propose an adaptive aggregation technique to improve the performance of data aggregation, for both uniform and non-uniform particle distributions. Furthermore, we enable efficient read operations by employing a level of detail re-ordering and a multi-resolution layout. Finally, we demonstrate the scalability of our techniques with experiments on large scale simulation workloads up to 256K cores on two different leadership supercomputers, Mira and Theta.
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Year | DOI | Field |
|---|---|---|
2019 | 10.1145/3337821.3337875 | Locality,Visualization,Level of detail,Computer science,Parallel computing,Parallel I/O,Data management,Data aggregator,Particle,Scalability |
DocType | ISSN | ISBN |
Conference | 978-1-4503-6295-5 | 978-1-4503-6295-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sidharth Kumar | 1 | 34 | 6.79 |
| Steve Petruzza | 2 | 3 | 3.79 |
| Will Usher | 3 | 27 | 9.39 |
| Valerio Pascucci | 4 | 3241 | 192.33 |