Particle methods parallel implementations by GP-GPU strategies

Rey Villaverde, Anton; Cercós Pita, Jose Luis; Souto Iglesias, Antonio y González Gutierrez, Leo Miguel (2011). Particle methods parallel implementations by GP-GPU strategies. En: "II International Conference on Particle-based Methods - Fundamentals and Applications PARTICLES 2011", 26/10/2011 - 28/10/2011, Barcelona, España. pp. 1-12.

Descripción

Título: Particle methods parallel implementations by GP-GPU strategies
Autor/es:
  • Rey Villaverde, Anton
  • Cercós Pita, Jose Luis
  • Souto Iglesias, Antonio
  • González Gutierrez, Leo Miguel
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: II International Conference on Particle-based Methods - Fundamentals and Applications PARTICLES 2011
Fechas del Evento: 26/10/2011 - 28/10/2011
Lugar del Evento: Barcelona, España
Título del Libro: Proceedings of II International Conference on Particle-based Methods - Fundamentals and Applications PARTICLES 2011
Fecha: 2011
Materias:
Escuela: E.T.S.I. Navales (UPM)
Departamento: Enseñanzas Básicas de la Ingeniería Naval [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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URL Oficial: http://congress.cimne.com/particles2011/frontal/default.asp

Resumen

This paper outlines the problems found in the parallelization of SPH (Smoothed Particle Hydrodynamics) algorithms using Graphics Processing Units. Different results of some parallel GPU implementations in terms of the speed-up and the scalability compared to the CPU sequential codes are shown. The most problematic stage in the GPU-SPH algorithms is the one responsible for locating neighboring particles and building the vectors where this information is stored, since these specific algorithms raise many dificulties for a data-level parallelization. Because of the fact that the neighbor location using linked lists does not show enough data-level parallelism, two new approaches have been pro- posed to minimize bank conflicts in the writing and subsequent reading of the neighbor lists. The first strategy proposes an efficient coordination between CPU-GPU, using GPU algorithms for those stages that allow a straight forward parallelization, and sequential CPU algorithms for those instructions that involve some kind of vector reduction. This coordination provides a relatively orderly reading of the neighbor lists in the interactions stage, achieving a speed-up factor of x47 in this stage. However, since the construction of the neighbor lists is quite expensive, it is achieved an overall speed-up of x41. The second strategy seeks to maximize the use of the GPU in the neighbor's location process by executing a specific vector sorting algorithm that allows some data-level parallelism. Al- though this strategy has succeeded in improving the speed-up on the stage of neighboring location, the global speed-up on the interactions stage falls, due to inefficient reading of the neighbor vectors. Some changes to these strategies are proposed, aimed at maximizing the computational load of the GPU and using the GPU texture-units, in order to reach the maximum speed-up for such codes. Different practical applications have been added to the mentioned GPU codes. First, the classical dam-break problem is studied. Second, the wave impact of the sloshing fluid contained in LNG vessel tanks is also simulated as a practical example of particle methods

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ID de Registro: 12713
Identificador DC: http://oa.upm.es/12713/
Identificador OAI: oai:oa.upm.es:12713
Depositado por: Memoria Investigacion
Depositado el: 18 Dic 2012 09:15
Ultima Modificación: 21 Abr 2016 11:59
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