Abstract
Frameworks that facilitate runtime data sharingacross multiple applications are of great importance for scientificdata analytics. Although existing frameworks work well overuniform mesh data, they can not effectively handle adaptive meshrefinement (AMR) data. Among the challenges to construct anAMR-capable framework include: (1) designing an architecturethat facilitates online AMR data management, (2) achievinga load-balanced AMR data distribution for the data stagingspace at runtime, and (3) building an effective online indexto support the unique spatial data retrieval requirements forAMR data. Towards addressing these challenges to supportruntime AMR data sharing across scientific applications, wepresent the AMRZone framework. Experiments over real-worldAMR datasets demonstrate AMRZone's effectiveness at achievinga balanced workload distribution, reading/writing large-scaledatasets with thousands of parallel processes, and satisfyingqueries with spatial constraints. Moreover, AMRZone's performance and scalability are even comparable with existing state-of-the-art work when tested over uniform mesh data with up to16384 cores, in the best case, our framework achieves a 46% performance improvement.
| Original language | English |
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| Title of host publication | Proceedings - 2016 16th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 116-125 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781509024520 |
| DOIs | |
| State | Published - Jul 18 2016 |
| Event | 16th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2016 - Cartagena, Colombia Duration: May 16 2016 → May 19 2016 |
Publication series
| Name | Proceedings - 2016 16th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2016 |
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Conference
| Conference | 16th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2016 |
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| Country/Territory | Colombia |
| City | Cartagena |
| Period | 05/16/16 → 05/19/16 |
Funding
We would like to thank the National Energy Research Scientific Computing Center and Oak Ridge National Laboratory for providing the computing resources. Oak Ridge National Laboratory is managed by UTBattelle for the LLC U.S. D.O.E. under Contract DE-AC05-00OR22725. Support for this work is provided by U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research and the U.S. National Science Foundation (Expeditions in Computing and EAGER programs). Work at the Lawrence Berkeley National Laboratory is supported by the Director, Office of Science, Office of Advanced Scientific Computing Research, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.