Optimizing a parameterized message-passing metaheuristic scheme on a heterogeneous cluster
- 81 Downloads
This paper studies the development of message-passing parameterized schemes of metaheuristics and the use of auto-tuning techniques to optimize their execution time. Previous parameterized schemes on shared-memory are extended with new metaheuristic-parallelism parameters representing the migration frequency, the size of the migration and the number of processes. An optimization Problem of Electricity Consumption in Exploitation of Wells is used as test case. Experimental results in heterogeneous systems are reported for this problem, and the influence of the parallelism parameters is studied. The message-passing scheme proves to be preferable to the shared-memory scheme in terms of execution time, giving similar results for the goodness of the solutions. In the executions in a heterogeneous cluster, the best experimental results are obtained in terms of speed-up and quality of the solution by mapping a number of processes close to the value of the population size, and considering the relative speeds of the components of the heterogeneous system. Furthermore, optimized execution times can be achieved with auto-tuning techniques based on theoretical–empirical models of the execution time.
KeywordsParameterized metaheuristic schemes Parallel metaheuristics Message-passing metaheuristic schemes Heterogeneous computing Auto-tuning
This work was supported by the Spanish MINECO, as well as European Commission FEDER funds, under Grant TIN2015-66972-C5-3-R.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Cantú-Paz E (1998) A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis 10Google Scholar
- Cutillas-Lozano J-M, Giménez D (2013) Determination of the kinetic constants of a chemical reaction in heterogeneous phase using parameterized metaheuristics. In: Proceedings of the international conference on computational science, pp 787–796Google Scholar
- Cutillas-Lozano J-M, Giménez D (2014) Optimizing shared-memory hyperheuristics on top of parameterized metaheuristics. In Proceedings of the international conference on computational science, pp 20–29Google Scholar
- Cutillas-Lozano L-G, Giménez D, Giménez D (2012) Modeling shared-memory metaheuristic schemes for electricity consumption. In: 9th International conference on distributed computing and artificial intelligence, pp 33–40Google Scholar
- Cutillas-Lozano J-M, Giménez D, Almeida F (2015) Hyperheuristics based on parametrized metaheuristic schemes. In: Proceedings of the genetic and evolutionary computation conference, pp 361–368Google Scholar
- Frigo M, Johnson SG (1998) FFTW: an adaptive software architecture for the FFT. IEEE Int Conf Acoust Speech Signal Process 3:1381–1384Google Scholar
- Imbernón B, Cecilia JM, Giménez D (2016) Enhancing metaheuristic-based virtual screening methods on massively parallel and heterogeneous systems. In: Proceedings of the 7th international workshop on programming models and applications for multicores and manycores, pp 50–58Google Scholar
- Katagiri T, Kise K, Honda H (2004) Effect of auto-tuning with user’s knowledge for numerical software. In: Vassiliadis JLGS, Piuri V (eds) Proceedings of the first conference on computing frontiers, pp 12–25Google Scholar
- Lässig J, Sudholt D (2011a) Adaptive population models for offspring populations and parallel evolutionary algorithms. In: Foundations of genetic algorithms, 11th international workshop, pp 181–192Google Scholar
- Lässig J, Sudholt D (2011b) Analysis of speedups in parallel evolutionary algorithms for combinatorial optimization—(extended abstract). In: Algorithms and computation—22nd international symposium, pp 405–414Google Scholar
- Mezmaz M-S, Kessaci Y, Lee YC, Melab N, Talbi E-G, Zomaya AY, Tuyttens D (2010) A parallel island-based hybrid genetic algorithm for precedence-constrained applications to minimize energy consumption and makespan. In: GRID, pp 274–281Google Scholar
- Raidl GR (2006) A unified view on hybrid metaheuristics. Hybrid metaheuristics, third international workshop, LNCS 4030:1–12Google Scholar
- Talbi E (2015) Parallel evolutionary combinatorial optimization. In: Springer handbook of, computational intelligence, pp 1107–1125Google Scholar
- Yu T, Sastry K, Goldberg DE (2005) Online population size adjusting using noise and substructural measurements. In: Proceedings of the IEEE congress on evolutionary computation, pp 2491–2498Google Scholar