A Tool for Scientific Visualization Based on Particle Tracing Algorithm on Graphics Processing Units
Resumo
The need to process large volumes of data associated with the increasing computing power contained in the current graphics cards have encouraged the academic community to produce techniques applied to parallel graphics processing designed for scientific applications. In this context, the goal of this paper is to present an implementation of a scientific visualization tool based on the Particle Tracing Algorithm on graphics processing units using, in particular, the Compute Unified Device Architecture (CUDA). It is also evaluated the performance gains obtained related the combination of some of the settings offered by CUDA.Referências
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A. Mittmann, M. Dantas, and A. von Wangenheim. Design and implementation of brain fiber tracking for gpus and pc clusters. In Computer Architecture and High Performance Computing, 2009. SBAC-PAD '09. 21st International Symposium on, pages 101- 108, 28-31 2009.
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F. Porto, G. A. Giraldi, J. C. de Oliveira, R. L. S. Silva, and B. Schulze. Codims: an adaptable middleware system for scientific visualization in grids. Concurrency - Practice and Experience, 16(5):515-522, 2004.
A. V. Xavier. Animação de fluidos via atômatos celulares e sistemas de partículas. Master's thesis, Laboratório Nacional de Computação Científica, 2006.
E. Camargo, P. J. Blanco, R. A. Feijóo, and R. L. S. Silva. Efficient implementation for particle tracing in computational hemodynamics. In Métodos Numéricos e Computacionais em Engenharia - CMNE CILAMCE 2009, Búzios, Brazil, november 2009.
S. Che, M. Boyer, J. Meng, D. Tarjan, J. W. Sheaffer, and K. Skadron. A performance study of general-purpose applications on graphics processors using cuda. Journal of Parallel and Distributed Computing, 68(10):1370-1380, 2008. General-Purpose Processing using Graphics Processing Units.
B. Coutinho, D. Sampaio, F. Pereira, and W. Meira. Performance debugging of gpgpu applications with the divergence map. In Computer Architecture and High Performance Computing (SBAC-PAD), 2010 22nd International Symposium on, pages 33-40, oct. 2010.
D. E. M. de Oliveira. Otimização das aplicações de visualização científica usando o qef. Master's thesis, Instituto Militar de Engenharia, 2010.
F. Hecht, P. J. Mucha, and G. Turk. Virtual rheoscopic fluids. IEEE Transactions on Visualization and Computer Graphics, 16:147-160, 2010.
P. Kondratieva, J. Krüger, and R. Westermann. The application of gpu particle tracing to diffusion tensor field visualization. In IEEE Visualization, 2005.
I. Larrabide and R. A. Feijóo. HeMoLab: Laboratório de Modelagem em Hemodinâmica. Technical Report 13/2006, Laboratório Nacional de Computação Científica, 2006.
A. Mittmann, M. Dantas, and A. von Wangenheim. Design and implementation of brain fiber tracking for gpus and pc clusters. In Computer Architecture and High Performance Computing, 2009. SBAC-PAD '09. 21st International Symposium on, pages 101- 108, 28-31 2009.
NVIDIA. CUDA Programming Guide. NVIDIA Corporation, 2701 San Tomas Expressway, Santa Clara, CA 95050, 3.2 edition, 10 2010.
F. Porto, G. A. Giraldi, J. C. de Oliveira, R. L. S. Silva, and B. Schulze. Codims: an adaptable middleware system for scientific visualization in grids. Concurrency - Practice and Experience, 16(5):515-522, 2004.
A. V. Xavier. Animação de fluidos via atômatos celulares e sistemas de partículas. Master's thesis, Laboratório Nacional de Computação Científica, 2006.
Publicado
26/10/2011
Como Citar
CAMARGO, Eduardo; KOSTIN, Sergio; PINTO, Raquel Coelho Gomes.
A Tool for Scientific Visualization Based on Particle Tracing Algorithm on Graphics Processing Units. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 12. , 2011, Vitória.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 73-80.
DOI: https://doi.org/10.5753/wscad.2011.17270.
