Distributed particle-based rendering framework for large data visualization on hpc environments

Jorji Nonaka, Naohisa Sakamoto, Takashi Shimizu, Masahiro Fujita, Kenji Ono, Koji Koyamada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we present a distributed data visualization framework for HPC environments based on the PBVR (Particle Based Volume Rendering) method. The PBVR method is a kind of point-based rendering approach where the volumetric data to be visualized is represented as a set of small and opaque particles. This method has the object-space and image-space variants, defined by the place (object or image- space) where the particle data sets are generated. We focused on the object-space approach, which has the advantage when handling large-scale simulation data sets such as those generated by modern HPC systems. In the object-space approach, the particle generation and the subsequent rendering processes can be easily decoupled. In this work, we took advantage of this separability to implement the proposed distributed rendering framework. The particle generation process utilizes the functionalities provided by the KVS (Kyoto Visualization System), and the particle rendering process utilizes the functionalities provided by the HIVE (Heterogeneously Integrated Visual- analytics Environment). The proposed distributed visualization framework is targeted to work also on systems without any hardware graphics acceleration capability, which are commonly found on modern HPC operational environments. We evaluated this PBVR-based distributed visualization infrastructure on the K computer operational environment by utilizing a CPU-only processing server for the particle data generation and rendering. In this preliminary evaluation, using some CFD (Computational Fluid Dynamics) simulation data sets, we obtained encouraging results for pushing further the development in order to make this system available as an effective visualization alternative for the HPC users.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017
EditorsWaleed W. Smari
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages300-307
Number of pages8
ISBN (Electronic)9781538632505
DOIs
Publication statusPublished - Sep 12 2017
Event15th International Conference on High Performance Computing and Simulation, HPCS 2017 - Genoa, Italy
Duration: Jul 17 2017Jul 21 2017

Publication series

NameProceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017

Conference

Conference15th International Conference on High Performance Computing and Simulation, HPCS 2017
CountryItaly
CityGenoa
Period7/17/177/21/17

Fingerprint

Data visualization
Data Visualization
Large Data
Volume rendering
Rendering
Visualization
Volume Rendering
Approach Space
Image Space
Program processors
Computational fluid dynamics
Servers
Hardware
Visual Analytics
Framework
Rendering (computer graphics)
Graphics Hardware
Computer simulation
Processing
Separability

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems and Management
  • Modelling and Simulation
  • Computer Networks and Communications
  • Computer Science (miscellaneous)

Cite this

Nonaka, J., Sakamoto, N., Shimizu, T., Fujita, M., Ono, K., & Koyamada, K. (2017). Distributed particle-based rendering framework for large data visualization on hpc environments. In W. W. Smari (Ed.), Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017 (pp. 300-307). [8035093] (Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPCS.2017.54

Distributed particle-based rendering framework for large data visualization on hpc environments. / Nonaka, Jorji; Sakamoto, Naohisa; Shimizu, Takashi; Fujita, Masahiro; Ono, Kenji; Koyamada, Koji.

Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017. ed. / Waleed W. Smari. Institute of Electrical and Electronics Engineers Inc., 2017. p. 300-307 8035093 (Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nonaka, J, Sakamoto, N, Shimizu, T, Fujita, M, Ono, K & Koyamada, K 2017, Distributed particle-based rendering framework for large data visualization on hpc environments. in WW Smari (ed.), Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017., 8035093, Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017, Institute of Electrical and Electronics Engineers Inc., pp. 300-307, 15th International Conference on High Performance Computing and Simulation, HPCS 2017, Genoa, Italy, 7/17/17. https://doi.org/10.1109/HPCS.2017.54
Nonaka J, Sakamoto N, Shimizu T, Fujita M, Ono K, Koyamada K. Distributed particle-based rendering framework for large data visualization on hpc environments. In Smari WW, editor, Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 300-307. 8035093. (Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017). https://doi.org/10.1109/HPCS.2017.54
Nonaka, Jorji ; Sakamoto, Naohisa ; Shimizu, Takashi ; Fujita, Masahiro ; Ono, Kenji ; Koyamada, Koji. / Distributed particle-based rendering framework for large data visualization on hpc environments. Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017. editor / Waleed W. Smari. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 300-307 (Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017).
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