A comparative study of high-productivity high-performance programming languages for parallel metaheuristics

Jan Gmys (1, 2) , Tiago Carneiro (3) , Nouredine Melab (3, 4) , El-Ghazali Talbi (3, 4) , Daniel Tuyttens (1, 2)

1 UMONS - University of Mons [Belgium]
2 Faculté polytechnique de Mons
3 BONUS - Optimisation de grande taille et calcul large échelle
4 Université de Lille

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.

Mots clés: Metaheuristics; Parallel metaheuristics; High-productivity languages; Parallel computing

Domaines: Calcul parallèle, distribué et partagé [cs.DC]; Algorithme et structure de données [cs.DS]; Recherche opérationnelle [math.OC]

Jan Gmys, Tiago Carneiro, Nouredine Melab, El-Ghazali Talbi, Daniel Tuyttens. A comparative study of high-productivity high-performance programming languages for parallel metaheuristics. Swarm and Evolutionary Computation, 2020, 57, ⟨10.1016/j.swevo.2020.100720⟩. ⟨hal-02879767⟩