Volume 6 Number 2 (Feb. 2011)
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JSW 2011 Vol.6(2): 298-305 ISSN: 1796-217X
doi: 10.4304/jsw.6.2.298-305

Large-Scale Vector Data Visualization Using High Performance Computing

Ahmed S. Ali1, Ashraf S. Hussein1, Mohamed F. Tolba1, Ahmed H. Yousef2
1Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
2Faculty of Engineering, Ain Shams University, Cairo, Egypt

Abstract—In computational flow visualization, integration based geometric flow visualization is often used to explore the flow field structure. A typical time-varying dataset from a Computational Fluid Dynamics (CFD) simulation can easily require hundreds of gigabytes to even terabytes of storage space, which creates challenges for the consequent data-analysis tasks. This paper presents new techniques for visualization of extremely large time-varying vector data using high performance computing. The high level requirements that guided the formulation of the new techniques are (a) support for large dataset sizes, (b) support for temporal coherence of the vector data, (c) support for distributed memory high performance computing and (d) optimum utilization of the computing nodes with multi-cores (multicore processors). The challenge is to design and implement techniques that meet these complex requirements and balance the conflicts between them. The fundamental innovation in this work is developing efficient distributed visualization for large time-varying vector data. The maximum performance was reached through the parallelization of multiple processes on the multiple cores of each computing node. Accuracy of the proposed techniques was confirmed compared to the benchmark results. In addition, the proposed techniques exhibited acceptable scalability for different data sizes with better scalability for the larger ones. Finally, the utilization of the computing nodes was satisfactory for the considered test cases.


Cite: Ahmed S. Ali, Ashraf S. Hussein, Mohamed F. Tolba, Ahmed H. Yousef, "Large-Scale Vector Data Visualization Using High Performance Computing," Journal of Software vol. 6, no. 2, pp. 298-305, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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