Improving the performance and energy of Non-Dominated Sorting for evolutionary multiobjective optimization on GPU/CPU platforms

Improving the performance and energy of Non-Dominated Sorting for evolutionary multiobjective... Non-Dominated Sorting (NDS) is the most time-consuming procedure used in the majority of evolutionary multiobjective optimization algorithms that are based on Pareto dominance ranking without regard to the computation time of the objective functions. It can be accelerated by the exploitation of its parallelism on High Performance Computing systems, that provide heterogeneous processing units, such as multicore processors and GPUs. The optimization of energy efficiency of such systems is a challenge in scientific computation since it depends on the kind of processing which is performed. Our interest is to solve NDS in an efficient way concerning both runtime and energy consumption. In literature, performance improvement has been extensively studied. Recently, a sequential Best Order Sort (BOS) algorithm for NDS has been introduced as one of the most efficient one in terms of practical performance. This work is focused on the acceleration of the NDS on modern architectures. Two efficient parallel NDS algorithms based on Best Order Sort, are introduced, MC-BOS and GPU-BOS. Both algorithms start with the fast sorting of population by objectives. MC-BOS computes in parallel the analysis of the population by objectives on the multicore processors. GPU-BOS is based on the principles of Best Order Sort, with a new scheme designed to harness the massive parallelism provided by GPUs. A wide experimental study of both algorithms on several kinds of CPU and GPU platforms has been carried out. Runtime and energy consumption are analysed to identify the best platform/algorithm of the parallel NDS for every particular population size. The analysis of obtained results defines criteria to help the user when selecting the optimal parallel version/platform for particular dimensions of NDS. The experimental results show that the new parallel NDS algorithms overcome the sequential Best Order Sort in terms of the performance and energy efficiency in relevant factors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Global Optimization Springer Journals

Improving the performance and energy of Non-Dominated Sorting for evolutionary multiobjective optimization on GPU/CPU platforms

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Mathematics; Optimization; Operations Research/Decision Theory; Real Functions; Computer Science, general
ISSN
0925-5001
eISSN
1573-2916
D.O.I.
10.1007/s10898-018-0669-3
Publisher site
See Article on Publisher Site

Abstract

Non-Dominated Sorting (NDS) is the most time-consuming procedure used in the majority of evolutionary multiobjective optimization algorithms that are based on Pareto dominance ranking without regard to the computation time of the objective functions. It can be accelerated by the exploitation of its parallelism on High Performance Computing systems, that provide heterogeneous processing units, such as multicore processors and GPUs. The optimization of energy efficiency of such systems is a challenge in scientific computation since it depends on the kind of processing which is performed. Our interest is to solve NDS in an efficient way concerning both runtime and energy consumption. In literature, performance improvement has been extensively studied. Recently, a sequential Best Order Sort (BOS) algorithm for NDS has been introduced as one of the most efficient one in terms of practical performance. This work is focused on the acceleration of the NDS on modern architectures. Two efficient parallel NDS algorithms based on Best Order Sort, are introduced, MC-BOS and GPU-BOS. Both algorithms start with the fast sorting of population by objectives. MC-BOS computes in parallel the analysis of the population by objectives on the multicore processors. GPU-BOS is based on the principles of Best Order Sort, with a new scheme designed to harness the massive parallelism provided by GPUs. A wide experimental study of both algorithms on several kinds of CPU and GPU platforms has been carried out. Runtime and energy consumption are analysed to identify the best platform/algorithm of the parallel NDS for every particular population size. The analysis of obtained results defines criteria to help the user when selecting the optimal parallel version/platform for particular dimensions of NDS. The experimental results show that the new parallel NDS algorithms overcome the sequential Best Order Sort in terms of the performance and energy efficiency in relevant factors.

Journal

Journal of Global OptimizationSpringer Journals

Published: Jun 5, 2018

References

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