Computational strategies for implementation of 2D elastic wave modeling in GPU

Authors

DOI:

https://doi.org/10.31908/19098367.2016

Keywords:

CPML, CUDA, Elastic wave modeling, GPU, HPC

Abstract

Elastic wave modeling presents a challenge to implement since it is a computationally costly procedure. Nowadays, due to GPU increased power jointly with development in HPC computation, it is possible to execute elastic modeling with better execution times and memory use. This study evaluates the performance of 2 strategies for implementing elastic modeling using different kernel launching layouts, CPML memory allocation strategies, and wavefield storage management. The performance measures show that the algorithm, which includes 2D kernel launching layout, CPML reduced memory strategy, and GPU global memory storage to save wavefield cube peaks up to 88.4% better execution time and uses 13.3 times less memory to obtain the same elastic modeling results. There is also an increasing trend of enhancement in execution times and memory savings when working with models of bigger sizes with this strategy.

Author Biographies

  • Anderson Páez Chanagá, Universidad Industrial de Santander

    Anderson Paez received the B.E.E degree in 2009 from Universidad Industrial de Santander, Bucaramanga, Colombia. He is currently developing M.Sc. studies at the same University. As professional engineer, he has worked in Instrumentation, Electrical and Control disciplines in Oil&Gas, Cement, and Electric power generation industries for different companies as SNC Lavalin, TGI, Termozipa and others; he also has worked in academy at UIS and SENA. He is a member of the Connectivity and signal processing group (CPS) at UIS, and his current research interest fields are High Performance Computing applications, machine learning, seismic data processing.

  • Ana Beatriz Ramirez Silva, Universidad Industrial de Santander

    Ana B. Ramirez received the B.E.E degree from the Universidad Industrial de Santander, Colombia; and the PhD degree in Electrical Engineering from University of Delaware, USA. Her research interest fields are seismic signal processing, compressive sensing, and acoustic medical imaging. She is currently Full Time Professor of the Electrical, Electronics and Communications Engineering department at Universidad Industrial de Santander, Colombia

  • Ivan Javier Sánchez Galvis, Universidad Industrial de Santander

    Ivan Sanchez received the B.E.E degree in 2014 and the M.Sc. degree in 2017, both from Universidad Industrial de Santander, Colombia. He is currently pursuing his Ph.D. in Engineering at the same University. His research interest fields are seismic signal processing, computational modeling, and machine learning. He is also currently a Lecturer of the Electrical, Electronics and Communications Engineering department at Universidad Industrial de Santander.

References

Alaei, B., “Seismic Modeling of Complex Geological Structures,” in Seismic Waves-Research and Analysis, [Online], M. Kanao, Ed. InTech, 2012, chp. 11. https://www.intechopen.com/books/seismic-waves-research-and-analysis.

Virieux, J., Asnaashari, A., Brossier, R., Métivier, L., Ribodetti, A., Zhou, W. (2017, Jan.) “An introduction to full waveform inversion,” in Encyclopedia of exploration geophysics [Online], Ed, Society of Exploration Geophysicists, 2017, R1-R40.

Permana, T., Sudarmaji, M. (2014, Oct.) “The 2D finite difference numerical modelling of P-Sv wave propagation in elastic heterogeneneous medium using graphic processing unit: Case study of mount Merapi topography, Yogyakarta,” in Proc International Conference on Physics 2014, pp. 74-85.

Keckler, S.W., Dally, W.J., Khailany, B., Garland, M., Glasco, D., (2011, Oct.) “Gpus and the future of parallel computing,” IEEE Micro 31(5), pp. 7-17.

Arora, M., “The architecture and evolution of cpu-gpu systems for general purpose computing,” Department of Computer Science and Engineering, University of California, San Diego 2012. [Online] Available: http://cseweb.ucsd.edu/~marora/files/papers/REReport_ManishArora.pdf.

Cheng, J., Grossman, M., Mcercher, T., “Professional CUDA C Programming.”, , Indiana ,Wiley and Sons,2014.

Virieux, J. (1986, Apr.) “P-sv wave propagation in heterogeneous media: Velocity-stress finite-difference method” Geophysics 51(4), pp. 889-1033.

Komatitsch, D., Martin, R., (2007, Sept.) “An unsplit convolutional perfectly matched layer improved at grazing incidence for the seismic wave equation,” Geophysics 72(5), pp. 1ZO-Z83.

Moczo, P., Robertsson, J.O., Eisner, L., (2007) “The finite-difference time-domain method for modeling of seismic wave propagation” Advances in geophysics 48, pp. 421-516.

CUDA C++ Programming Guide, Nvidia Corp. 2020.

CUDA C++ Best Practices Guide, Nvidia Corp. 2020.

Brittan, J., Bai, J., Delome, H., Wang, C., Yingst, D., (2013, Oct.) “Full waveform inversion-the state of the art,” First Break 31(10), pp. 75-81. Available: https://www.iongeo.com/virtuals/ResourceArchives/content/documents/Resource%20Center/Articles/FB_Full_Waveform_Inversion_131011.pdf.

Menke, W., “Geophysical Data Analysis”, 4th Ed, Elsevier Inc, 2018.

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Published

2020-12-21

Issue

Vol. 14 No. 28 (2020)

Section

Artículos

How to Cite

Computational strategies for implementation of 2D elastic wave modeling in GPU. (2020). Entre Ciencia E ingeniería, 14(28), 52-58. https://doi.org/10.31908/19098367.2016