Leveraging heterogeneous parallel platform in solving hard discrete optimization problems with metaheuristics

Pietroń, Marcin; Byrski, Aleksander; Kisiel-Dorohinicki, Marek

doi:10.1016/j.jocs.2016.07.008

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Then, it selects the best move. On the other hand, solution quality has been considered in works like [63,64] with a small acceleration factor of at best 1.19x [63]. While in works like [32,35,54,88], the behavior of GPU parallel implementations is not modified compared to the CPU implementations (quality not improved) but a significant acceleration factor of at best 696x [54] is achieved.…”

Section: Discussionmentioning

confidence: 99%

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GPU parallelization strategies for metaheuristics: a survey

Essaid

Idoumghar

Lepagnot

et al. 2018

International Journal of Parallel, Emergent and Distributed Sys

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Metaheuristics have been showing interesting results in solving hard optimization problems. However, they become limited in terms of effectiveness and runtime for high dimensional problems. Thanks to the independency of metaheuristics components, parallel computing appears as an attractive choice to reduce the execution time and to improve solution quality. By exploiting the increasing performance and programability of graphics processing units (GPUs) to this aim, GPU-based parallel metaheuristics have been implemented using different designs. Recent results in this area show that GPUs tend to be effective co-processors for leveraging complex optimization problems. In this survey, mechanisms involved in GPU programming for implementing parallel metaheuristics are presented and discussed through a study of relevant research papers.

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Section: Discussionmentioning

confidence: 99%

“…Their GPGPU implementation shows that it is 10 times faster than an optimized multicore CPU. According to [63], one of the suited components of SS to parallelism is the improvement method. To implement the improvement method, two algorithms are tested: random mutation hill climbing and simulated annealing.…”

Section: Other Evolutionary Algorithmsmentioning

confidence: 99%