Particle methods parallel implementations by GP-GPU strategies

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

Description

Title: Particle methods parallel implementations by GP-GPU strategies
Author/s:
  • Rey Villaverde, Anton
  • Cercós Pita, Jose Luis
  • Souto Iglesias, Antonio
  • González Gutierrez, Leo Miguel
Item Type: Presentation at Congress or Conference (Article)
Event Title: II International Conference on Particle-based Methods - Fundamentals and Applications PARTICLES 2011
Event Dates: 26/10/2011 - 28/10/2011
Event Location: Barcelona, España
Title of Book: Proceedings of II International Conference on Particle-based Methods - Fundamentals and Applications PARTICLES 2011
Date: 2011
Subjects:
Faculty: E.T.S.I. Navales (UPM)
Department: Enseñanzas Básicas de la Ingeniería Naval [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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

More information

Item ID: 12713
DC Identifier: http://oa.upm.es/12713/
OAI Identifier: oai:oa.upm.es:12713
Official URL: http://congress.cimne.com/particles2011/frontal/default.asp
Deposited by: Memoria Investigacion
Deposited on: 18 Dec 2012 09:15
Last Modified: 21 Apr 2016 11:59
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