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An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)

Abstract

Based on previous work on in-situ data transfer infrastructure and compiler-based software analysis, we have designed a virtual observation system for real time computer simulations. This paper presents an event detection framework for a virtual observation system. By using signal processing and detection approaches to the memory-based data streams, this framework can be reconfigured to capture high-frequency events and low-frequency events. These approaches used in the framework can dramatically reduce the data transfer needed for in-situ data analysis (between distributed computing nodes or between the CPU/GPU nodes). In the paper, we also use a terrestrial ecosystem system simulation within the Earth System Model to demonstrate the practical values of this effort.

Notes

Acknowledgements

This research was funded by the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER) program, and Advanced Scientific Computing Research (ASCR) program, and LDRD #8389. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

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Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018

Authors and Affiliations

  • Zhuo Yao
    • 1
  • Dali Wang
    • 1
    • 2
  • Yifan Wang
    • 1
  • Fengming Yuan
    • 2
  1. 1.Department of Electric Engineering and Computer ScienceUniversity of TennesseeKnoxvilleUSA
  2. 2.Environmental Science DepartmentOak Ridge National LaboratoryOak RidgeUSA

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