Towards real-time detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma

doi:10.1109/TBDATA.2016.2599929

Title: Towards real-time detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma

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Abstract

A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. Here, on a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.

Authors:

Wu, Lingfei ^[1]; Wu, Kesheng ^[2]; Sim, Alex ^[2]; Churchill, Michael ^[3]; Choi, Jong Youl ^[4]; Stathopoulos, Andreas ^[1]; Chang, Choong -Seock ^[3]; Klasky, Scott A. ^[4]

College of William and Mary, Williamsburg, VA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:: 2016-06-01

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Org.:: USDOE Office of Science (SC)

OSTI Identifier:: 1326546

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Big Data

Additional Journal Information:: Journal Volume: 2; Journal Issue: 3; Journal ID: ISSN 2332-7790

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 70 PLASMA PHYSICS AND FUSION TECHNOLOGY; 97 MATHEMATICS AND COMPUTING; big data analytics; spatio-temporal feature; real-time detection and tracking; blob-filaments; fusion plasma

Citation Formats


                    Wu, Lingfei, Wu, Kesheng, Sim, Alex, Churchill, Michael, Choi, Jong Youl, Stathopoulos, Andreas, Chang, Choong -Seock, and Klasky, Scott A. Towards real-time detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma.  United States: N. p., 2016. 
        Web.  doi:10.1109/TBDATA.2016.2599929.

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                    Wu, Lingfei, Wu, Kesheng, Sim, Alex, Churchill, Michael, Choi, Jong Youl, Stathopoulos, Andreas, Chang, Choong -Seock, & Klasky, Scott A. Towards real-time detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma.  United States.  doi:10.1109/TBDATA.2016.2599929.

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                    Wu, Lingfei, Wu, Kesheng, Sim, Alex, Churchill, Michael, Choi, Jong Youl, Stathopoulos, Andreas, Chang, Choong -Seock, and Klasky, Scott A. Wed .  
        "Towards real-time detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma".  United States.  doi:10.1109/TBDATA.2016.2599929.  https://www.osti.gov/servlets/purl/1326546.

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@article{osti_1326546,

  title        = {Towards real-time detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma},

  author       = {Wu, Lingfei and Wu, Kesheng and Sim, Alex and Churchill, Michael and Choi, Jong Youl and Stathopoulos, Andreas and Chang, Choong -Seock and Klasky, Scott A.},

  abstractNote = {A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. Here, on a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.},

  doi          = {10.1109/TBDATA.2016.2599929},

  journal      = {IEEE Transactions on Big Data},

  number       = 3,

  volume       = 2,

  place        = {United States},

  year         = {2016},

  month        = {6}

}

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