Cluster Computing

, Volume 18, Issue 1, pp 29–40 | Cite as

In-situ feature-based objects tracking for data-intensive scientific and enterprise analytics workflows

  • Solomon Lasluisa
  • Fan Zhang
  • Tong Jin
  • Ivan Rodero
  • Hoang Bui
  • Manish Parashar


Emerging scientific simulations on leadership class systems are generating huge amounts of data and processing this data in an efficient and timely manner is critical for generating insights from the simulations. However, the increasing gap between computation and disk I/O speeds makes traditional data analytics pipelines based on post-processing cost prohibitive and often infeasible. In this paper, we investigate an alternate approach that aims to bring the analytics closer to the data using in-situ execution of data analysis operations. Specifically, we present the design, implementation and evaluation of a framework that can support in-situ feature-based objects tracking on distributed scientific datasets. Central to this framework is a scalable decentralized and online clustering, a cluster tracking algorithm, which executes in-situ (on different cores) in parallel with the simulation processes, and retrieves data from the simulations directly via on-chip shared memory. The results from our experimental evaluation demonstrate that the in-situ approach significantly reduces the cost of data movement, that the presented framework can support scalable feature-based objects tracking, and that it can be effectively used for in-situ analytics in large scale simulations.


Simulations workflows Scientific data analysis Scalable in-situ data analytics Feature-based objects tracking 



The research presented in this work is supported in part by US National Science Foundation (NSF) via Grants numbers OCI 1310283, DMS 1228203, IIP 0758566, OCI 1339036 and CNS 1305375, by the Director, Office of Advanced Scientific Computing Research, Office of Science, of the U.S. Department of Energy through the Scientific Discovery through Advanced Computing (SciDAC) Institute of Scalable Data Management, Analysis and Visualization (SDAV) under ward number DE-SC0007455, the Advanced Scientific Computing Research and Fusion Energy Sciences Partnership for Edge Physics Simulations (EPSI) under award number DE-FG02-06ER54857, the ExaCT Combustion Co-Design Center via subcontract number 4000110839 from UT Battelle, and by an IBM Faculty Award. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) under project number TG-CCR110035, which is supported by NSF grant number OCI 1053575. The research was conducted as part of the NSF Cloud and Autonomic Computing (CAC) Center at Rutgers University and the Rutgers Discovery Informatics Institute (RDI2). We thank Dr. Deborah Silver and Sedat Ozer for useful discussions on data visualization and providing the scientific dataset for our experimental evaluation.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Solomon Lasluisa
    • 1
  • Fan Zhang
    • 1
  • Tong Jin
    • 1
  • Ivan Rodero
    • 1
  • Hoang Bui
    • 1
  • Manish Parashar
    • 1
  1. 1.Rutgers Discovery Informatics Institute, NSF Cloud and Autonomic Computing CenterRutgers UniversityPiscatawayUSA

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