Using multiple graphics accelerators to solve the two-dimensional inverse heat conduction problem

In this paper, we present a novel parallel algorithm implemented on graphics accelerators to solve the two-dimensional inverse heat conduction problem (estimation of the temporo-spatial heat transfer coefficients without any prior knowledge of the thermal boundary conditions) based on Particle Swarm...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering Vol. 336; pp. 286 - 303
Main Authors: Szénási, Sándor, Felde, Imre
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.07.2018
Elsevier BV
Subjects:
Algorithms
Conduction heating
Conductive heat transfer
Data parallel algorithm
Divergence
Genetic algorithm
Genetic algorithms
Graphics accelerators
Graphics processing units
Heat conductivity
Heat transfer coefficients
Heuristic
Heuristic methods
Inverse heat conduction problem
Inverse problems
Launching
Mathematical analysis
Particle accelerators
Particle Swarm Optimisation
Warp
Inverse heat conduction problem
Particle Swarm Optimisation
Data parallel algorithm
Genetic algorithm
Graphics accelerators
ISSN:0045-7825, 1879-2138
Online Access:Get full text
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Summary:In this paper, we present a novel parallel algorithm implemented on graphics accelerators to solve the two-dimensional inverse heat conduction problem (estimation of the temporo-spatial heat transfer coefficients without any prior knowledge of the thermal boundary conditions) based on Particle Swarm Optimisation method. In the absence of analytical solutions, there are several heuristic methods to solve the problem, but the unacceptable high runtime (several days) makes these unsuitable for practical use. This paper presents the methods on how to adapt the original sequential algorithm to an efficient data-parallel one, keeping in mind the main features of graphics processing units (launching multiple threads on all multiprocessors, storing data in fast on-chip memory, eliminating warp divergence and memory transfer latency, using the host and device together, etc.). The achieved ∼45× speed-up (without any accuracy degradation) makes the heuristic methods suitable for practical use. Some of the proposed ideas are generally useable; therefore, this paper can be considered a step-by-step guide for researchers of other fields to speed-up general purpose calculations and evaluate the results. •Our goal is to solve the two-dimensional IHCP using heuristic optimisation methods.•A multi-level parallel algorithm implemented on GPU and CPU cores is presented.•It is 100x faster than the original sequential one (the accuracy is the same).•Details of optimisation (memory transfers, warp divergence, etc.) are discussed.
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ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2018.03.024