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

Journal Article · 31 May 2016 · IEEE Transactions on Big Data

DOI: https://doi.org/10.1109/TBDATA.2016.2599929 · OSTI ID:1326546

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]

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

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1326546
• Alternate ID(s): OSTI ID: 1377450
• Journal Information: IEEE Transactions on Big Data, Journal Name: IEEE Transactions on Big Data Journal Issue: 3 Vol. 2; ISSN 2332-7790
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English