1,735 research outputs found
Reducing memory requirements for large size LBM simulations on GPUs
The scientific community in its never-ending road of larger and more efficient computational resources is in need of more efficient implementations that can adapt efficiently on the current parallel platforms. Graphics processing units are an appropriate platform that cover some of these demands. This architecture presents a high performance with a reduced cost and an efficient power consumption. However, the memory capacity in these devices is reduced and so expensive memory transfers are necessary to deal with big problems. Today, the lattice-Boltzmann method (LBM) has positioned as an efficient approach for Computational Fluid Dynamics simulations. Despite this method is particularly amenable to be efficiently parallelized, it is in need of a considerable memory capacity, which is the consequence of a dramatic fall in performance when dealing with large simulations. In this work, we propose some initiatives to minimize such demand of memory, which allows us to execute bigger simulations on the same platform without additional memory transfers, keeping a high performance. In particular, we present 2 new implementations, LBM-Ghost and LBM-Swap, which are deeply analyzed, presenting the pros and cons of each of them.This project was funded by the Spanish Ministry of Economy and Competitiveness (MINECO): BCAM Severo Ochoa accreditation SEV-2013-0323, MTM2013-40824, Computación de Altas Prestaciones VII TIN2015-65316-P, by the Basque Excellence Research Center (BERC 2014-2017) pro-
gram by the Basque Government, and by the Departament d' Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programació i Entorns d' Execució Paral·lels (2014-SGR-1051). We also thank the support of the computing facilities of Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT) and NVIDIA GPU Research Center program for the provided resources,
as well as the support of NVIDIA through the BSC/UPC NVIDIA GPU Center of Excellence.Peer ReviewedPostprint (author's final draft
Parallel Excluded Volume Tempering for Polymer Melts
We have developed a technique to accelerate the acquisition of effectively
uncorrelated configurations for off-lattice models of dense polymer melts which
makes use of both parallel tempering and large scale Monte Carlo moves. The
method is based upon simulating a set of systems in parallel, each of which has
a slightly different repulsive core potential, such that a thermodynamic path
from full excluded volume to an ideal gas of random walks is generated. While
each system is run with standard stochastic dynamics, resulting in an NVT
ensemble, we implement the parallel tempering through stochastic swaps between
the configurations of adjacent potentials, and the large scale Monte Carlo
moves through attempted pivot and translation moves which reach a realistic
acceptance probability as the limit of the ideal gas of random walks is
approached. Compared to pure stochastic dynamics, this results in an increased
efficiency even for a system of chains as short as monomers, however
at this chain length the large scale Monte Carlo moves were ineffective. For
even longer chains the speedup becomes substantial, as observed from
preliminary data for
Regularized lattice Boltzmann Multicomponent models for low Capillary and Reynolds microfluidics flows
We present a regularized version of the color gradient lattice Boltzmann (LB)
scheme for the simulation of droplet formation in microfluidic devices of
experimental relevance. The regularized version is shown to provide
computationally efficient access to Capillary number regimes relevant to
droplet generation via microfluidic devices, such as flow-focusers and the more
recent microfluidic step emulsifier devices.Comment: 9 pages, 5 figure
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