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

Wu, Lingfei; Wu, Kesheng; Sim, Alex; Churchill, Michael; Choi, Jong Youl; Stathopoulos, Andreas; Chang, Choong -Seock; Klasky, Scott A.

doi:10.1109/TBDATA.2016.2599929

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

Full Record
Other Related Research

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.

Copy to clipboard


                    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.  https://doi.org/10.1109/TBDATA.2016.2599929

Copy to clipboard


                    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.  https://doi.org/10.1109/TBDATA.2016.2599929.  https://www.osti.gov/servlets/purl/1326546.

Copy to clipboard


                    
@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}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1109/TBDATA.2016.2599929

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma [Spatio-Temporal Features in Large Irregular Data: Blob-Filaments in Fusion Plasma]

Journal Article Wu, Lingfei ; Wu, Kesheng John ; Sim, Alex ; ... - IEEE Transactions on Big Data

This work was originally motivated the need to identify and track blob filaments, a feature often associated with instability in magnetically confined fusion plasma. Understanding and mitigating such instability would improve fusion reactors and make fusion a truly inexhaustible source of clean energy. 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 overlappingmore »« less
https://doi.org/10.1109/TBDATA.2016.2599929

Full Text Available
Tracking blobs in the turbulent edge plasma of a tokamak fusion device

Journal Article Han, Woonghee ; Pietersen, Randall A. ; Villamor-Lora, Rafael ; ... - Scientific Reports

Abstract The analysis of turbulence in plasmas is fundamental in fusion research. Despite extensive progress in theoretical modeling in the past 15 years, we still lack a complete and consistent understanding of turbulence in magnetic confinement devices, such as tokamaks. Experimental studies are challenging due to the diverse processes that drive the high-speed dynamics of turbulent phenomena. This work presents a novel application of motion tracking to identify and track turbulent filaments in fusion plasmas, called blobs, in a high-frequency video obtained from Gas Puff Imaging diagnostics. We compare four baseline methods (RAFT, Mask R-CNN, GMA, and Flow Walk) trainedmore »« less
https://doi.org/10.1038/s41598-022-21671-w
High-performance epistasis detection in quantitative trait GWAS

Journal Article Weeks, Nathan T. ; Luecke, Glenn R. ; Groth, Brandon M. ; ... - International Journal of High Performance Computing Applications

epiSNP is a program for identifying pairwise single nucleotide polymorphism (SNP) interactions (epistasis) in quantitative-trait genome-wide association studies (GWAS). A parallel MPI version (EPISNPmpi) was created in 2008 to address this computationally expensive analysis on large data sets with many quantitative traits and SNP markers. However, the falling cost of genotyping has led to an explosion of large-scale GWAS data sets that challenge EPISNPmpi’s ability to compute results in a reasonable amount of time. Therefore, we optimized epiSNP for modern multi-core and highly parallel many-core processors to efficiently handle these large data sets. This paper describes the serial optimizations, dynamicmore »« less
Cited by 13
https://doi.org/10.1177/1094342016658110

Full Text Available
Tuning HDF5 subfiling performance on parallel file systems

Conference Byna, Suren ; Chaarawi, Mohamad ; Koziol, Quincey ; ...

Subfiling is a technique used on parallel file systems to reduce locking and contention issues when multiple compute nodes interact with the same storage target node. Subfiling provides a compromise between the single shared file approach that instigates the lock contention problems on parallel file systems and having one file per process, which results in generating a massive and unmanageable number of files. In this paper, we evaluate and tune the performance of recently implemented subfiling feature in HDF5. In specific, we explain the implementation strategy of subfiling feature in HDF5, provide examples of using the feature, and evaluate andmore »« less
Full Text Available
A Novel Spatio-Temporal Regime Tracking Method for Impact Simulations

Technical Report Harwell, Megan Louise

In this proposal, we present a novel method of tracking the rheological regimes activated during impact cratering events that will allow researchers to gain new insights into cratering mechanics. Rheology describes the stress-strain response of rocks to different conditions. Planetary impact cratering events often occur on too large of a scale to be feasibly captured in controlled experiments. Instead, these dynamic events are primarily studied using multi-physics codes equipped with complex material models that enable calculations of the impact event at scale. However, determining which physical processes are required for the problem of interest is challenging. Because the dominant rheologicalmore »« less
https://doi.org/10.2172/1998099

Full Text Available

Similar Records