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Expedient Modal Decomposition of Massive Datasets Using High Performance Computing Clusters

Vyapamakula Sreeramachandra, Sankeerth
http://rave.ohiolink.edu/etdc/view?acc_num=osu151515633114873

Abstract Details

2018, Master of Science, Ohio State University, Computer Science and Engineering.
High-fidelity observations of non-linear dynamical systems that are of practical interest lead to massive data sets which do not fit on a single computing node. Therefore, modal decomposition techniques must be able to exploit the capability of high-performance computing (HPC) facilities. Proper Orthogonal Decomposition and Sparse Coding are two of the commonly used modal decomposition techniques to obtain reduced order models. The goal of the research is to parallelize and implement these algorithms so that they can be used on high-performance computing clusters in order to expedite the process of modal decomposition from massive data sets. However, computation on various machines is associated with high memory usage and significant communication cost. Moreover, the overall computational cost is sensitive to the type of data set and various parameters of the algorithm. Therefore, several strategies are discussed and implementations are developed to address these constraints to perform expedient modal decomposition. Furthermore, a systematic study is performed over multiple data sets to assess the performance and scalability of the implementations.
Jack McNamara (Committee Member)
Sadayappan P (Advisor)
100 p.
Aerospace Engineering; Computer Science
high-performance computing clusters, PCA, Proper Orthogonal Decomposition, Sparse Coding, POD, ScaLapack, big data, distributed systems, parallel, Modal Decomposition,

Recommended Citations

Citations

  • Vyapamakula Sreeramachandra, S. (2018). Expedient Modal Decomposition of Massive Datasets Using High Performance Computing Clusters [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu151515633114873

    APA Style (7th edition)

  • Vyapamakula Sreeramachandra, Sankeerth. Expedient Modal Decomposition of Massive Datasets Using High Performance Computing Clusters. 2018. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu151515633114873.

    MLA Style (8th edition)

  • Vyapamakula Sreeramachandra, Sankeerth. "Expedient Modal Decomposition of Massive Datasets Using High Performance Computing Clusters." Master's thesis, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu151515633114873

    Chicago Manual of Style (17th edition)

osu151515633114873
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This open access ETD is published by The Ohio State University and OhioLINK.