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

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.

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Laboratory, Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1463973
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Journal Volume: 10861; Conference: International Conference on Computational Science (ICCS 2018) - Wuxi, , China - 6/11/2018 8:00:00 AM-6/14/2018 8:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Yao, Cindy, Wang, Dali, Wang, Yifan, and Yuan, Fengming. An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation. United States: N. p., 2018. Web. doi:10.1007/978-3-319-93701-4_4.
Yao, Cindy, Wang, Dali, Wang, Yifan, & Yuan, Fengming. An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation. United States. doi:10.1007/978-3-319-93701-4_4.
Yao, Cindy, Wang, Dali, Wang, Yifan, and Yuan, Fengming. Fri . "An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation". United States. doi:10.1007/978-3-319-93701-4_4. https://www.osti.gov/servlets/purl/1463973.
@article{osti_1463973,
title = {An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation},
author = {Yao, Cindy and Wang, Dali and Wang, Yifan and Yuan, Fengming},
abstractNote = {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.},
doi = {10.1007/978-3-319-93701-4_4},
journal = {},
issn = {0302-9743},
number = ,
volume = 10861,
place = {United States},
year = {2018},
month = {6}
}

Conference:
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