A performance analysis of a mimetic finite difference scheme for acoustic wave propagation on GPU platforms
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Título: | A performance analysis of a mimetic finite difference scheme for acoustic wave propagation on GPU platforms |
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Autor/es: | Otero Calviño, Beatriz | Francés, Jorge | Rodriguez, Robert | Rojas, Otilio | Solano, Freysimar | Guevara-Jordan, Juan |
Grupo/s de investigación o GITE: | Holografía y Procesado Óptico |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Física Aplicada a las Ciencias y las Tecnologías | Universitat Politècnica de Cataluña. Departament d'Arquitectura de Computadors | Universitat Politècnica de Cataluña. Escola Tècnica Superior d’Enginyeria de Telecomunicació de Barcelona | Universidad Central de Venezuela. Facultad de Ciencias | Barcelona Supercomputing Center. Department of Computer Applications in Science and Engineering |
Palabras clave: | Acoustic waves | Mimetic finite differences | SIMD extensions | GPU | CUDA programming |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores | Física Aplicada |
Fecha de creación: | 25-nov-2015 |
Fecha de publicación: | 25-feb-2017 |
Editor: | John Wiley & Sons |
Cita bibliográfica: | Concurrency and Computation: Practice and Experience. 2017, 29(4): e3880. doi:10.1002/cpe.3880 |
Resumen: | Realistic applications of numerical modeling of acoustic wave dynamics usually demand high-performance computing because of the large size of study domains and demanding accuracy requirements on simulation results. Forward modeling of seismic motion on a given subsurface geological structure is by itself a good example of such applications, and when used as a component of seismic inversion tools or as a guide for the design of seismic surveys, its computational cost increases enormously. In the case of finite difference methods (or any other volumen-discretization scheme), memory and computing demands rise with grid refinement, which may be necessary to reduce errors on numerical wave patterns and better capture target physical devices. In this work, we present several implementations of a mimetic finite difference method for the simulation of acoustic wave propagation on highly dense staggered grids. These implementations evolve as different optimization strategies are employed starting from appropriate setting of compilation flags, code vectorization by using streaming SIMD extensions Advanced Vector Extensions (AVX), CPU parallelization by exploiting the Open Multi-Processing framework to the final code parallelization on graphics processing unit platforms. We present and discuss the increasing processing speed up of this mimetic scheme achieved by the gradual implementation and testing of all these performance optimizations. In terms of simulation times, the performance of our graphics processing unit parallel implementations is consistently better than the best CPU version. |
Patrocinador/es: | Authors from Universidad Central de Venezuela (UCV) were partially supported by: Consejo de Desarrollo Científico y Humanístico de la UCV, Vice-rectorado Académico de la UCV, Coordinación de Investigación de la Facultad de Ciencias UCV, Banco Central de Venezuela (BCV) and Generalitat Valenciana under project PROMETEOII/2015/015. |
URI: | http://hdl.handle.net/10045/62068 |
ISSN: | 1532-0626 (Print) | 1532-0634 (Online) |
DOI: | 10.1002/cpe.3880 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2016 John Wiley & Sons, Ltd. |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.1002/cpe.3880 |
Aparece en las colecciones: | INV - GHPO - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2017_Otero_etal_ConcurrencyComputat_final.pdf | Versión final (acceso restringido) | 936,48 kB | Adobe PDF | Abrir Solicitar una copia |
2017_Otero_etal_ConcurrencyComputat_rev.pdf | Versión revisada (acceso abierto) | 807,77 kB | Adobe PDF | Abrir Vista previa |
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