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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
A comparative study of high-productivity high-performance programming languages for parallel metaheuristics
Swarm and Evolutionary Computation, Volume 57, Article 100720, Year 2020
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Description
Parallel metaheuristics require programming languages that provide both, high performance and a high level of programmability. This paper aims at providing a useful data point to help practitioners gauge the difficult question of whether to invest time and effort into learning and using a new programming language. To accomplish this objective, three productivity-aware languages (Chapel, Julia, and Python) are compared in terms of performance, scalability and productivity. To the best of our knowledge, this is the first time such a comparison is performed in the context of parallel metaheuristics. As a test-case, we implement two parallel metaheuristics in three languages for solving the 3D Quadratic Assignment Problem (Q3AP), using thread-based parallelism on a multi-core shared-memory computer. We also evaluate and compare the performance of the three languages for a parallel fitness evaluation loop, using four different test-functions with different computational characteristics. Besides providing a comparative study, we give feedback on the implementation and parallelization process in each language. © 2020 Elsevier B.V.
Authors & Co-Authors
Melab, Nouredine
France, Villeneuve-d'ascq
Centre de Recherche en Informatique, Signal et Automatique de Lille Cristal
France, Le Chesnay
Inria Institut National de Recherche en Informatique et en Automatique
Talbi, Emna Ghazali
France, Villeneuve-d'ascq
Centre de Recherche en Informatique, Signal et Automatique de Lille Cristal
France, Le Chesnay
Inria Institut National de Recherche en Informatique et en Automatique
Tuyttens, Daniel
Belgium, Mons
Université de Mons
Statistics
Citations: 25
Authors: 3
Affiliations: 3
Identifiers
Doi:
10.1016/j.swevo.2020.100720
ISSN:
22106502