High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

Parallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA...

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Bibliographic Details
Published in:International journal of parallel programming Vol. 49; no. 6; pp. 776 - 801
Main Authors: de Melo Menezes, Breno A., Herrmann, Nina, Kuchen, Herbert, Buarque de Lima Neto, Fernando
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2021
Springer Nature B.V
Subjects:
2020
Algorithms
Ant colony optimization
Computer Science
Domain specific languages
Optimization
Parallel programming
Processor Architectures
Software Engineering/Programming and Operating Systems
Special Issue on High-Level Parallel Programming and Applications
Swarm intelligence
Theory of Computation
High performance computing
Ant colony optimization
Algorithmic skeletons
ISSN:0885-7458, 1573-7640
Online Access:Get full text
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Summary:Parallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-021-00714-1