By Topic

Implementation and optimization of a thermal Lattice Boltzmann algorithm on a multi-GPU cluster

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Bertazzo, A. ; Univ. di Ferrara, Ferrara, Italy ; Mantovani, F. ; Pivanti, M. ; Pozzati, F.
more authors

Lattice Boltzmann (LB) methods are widely used today to describe the dynamics of fluids. Key advantages of this approach are the relative ease with which complex physics behavior, e.g. associated to multi-phase flows or irregular boundary conditions can be modeled, and - from a computational perspective - the large degree of available parallelism, that can be easily exploited on massively parallel systems. The advent of multi-core and many-core processors, including General Purpose Graphics Processing Unit (GP-GPU), has pushed the quest for parallelization also at the intra-processor level. From this point of view, LB methods may strongly benefit from these new architectures. In this paper we describe the implementation and optimization of a recently proposed thermal LB model - the so called D2Q37 model - on multi-GPU systems. We describe in details the optimization techniques that we have used at both the intra-processor and inter-processor level, present performance and scaling figures and analyze bottlenecks associated to this implementation.

Published in:

Innovative Parallel Computing (InPar), 2012

Date of Conference:

13-14 May 2012