High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons
Parallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA...
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| Published in: | International journal of parallel programming Vol. 49; no. 6; pp. 776 - 801 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.12.2021
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| ISSN: | 0885-7458, 1573-7640 |
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| Abstract | Parallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of
Algorithmic Skeletons
simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language
Musket
can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that
Musket
suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects,
Musket
generates high performance code with similar execution times when compared to low-level implementations. |
|---|---|
| AbstractList | Parallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of
Algorithmic Skeletons
simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language
Musket
can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that
Musket
suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects,
Musket
generates high performance code with similar execution times when compared to low-level implementations. Parallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations. |
| Author | de Melo Menezes, Breno A. Kuchen, Herbert Buarque de Lima Neto, Fernando Herrmann, Nina |
| Author_xml | – sequence: 1 givenname: Breno A. orcidid: 0000-0002-7010-7482 surname: de Melo Menezes fullname: de Melo Menezes, Breno A. email: breno.menezes@uni-muenster.de organization: University of Münster – sequence: 2 givenname: Nina surname: Herrmann fullname: Herrmann, Nina organization: University of Münster – sequence: 3 givenname: Herbert surname: Kuchen fullname: Kuchen, Herbert organization: University of Münster – sequence: 4 givenname: Fernando surname: Buarque de Lima Neto fullname: Buarque de Lima Neto, Fernando organization: University of Pernambuco |
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| Cites_doi | 10.1007/s10766-016-0416-7 10.1109/MCI.2006.329691 10.1007/11549468_83 10.1080/17445760.2013.842568 10.1002/9781119332015.ch13 10.1002/9780470496916 10.1109/CEC.2019.8790073 10.1016/j.jpdc.2012.01.002 10.1057/palgrave.jors.2601771 10.1057/jors.1990.166 10.1007/s11227-019-02825-6 10.1504/IJHPCN.2012.046370 10.1016/j.trc.2015.02.013 10.1007/s11227-019-02824-7 10.1007/BF00226291 10.1016/j.ejor.2016.04.030 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2021 The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| References | DorigoMBirattariMStutzleTAnt colony optimizationIEEE Comput. Intell. Mag.200614283910.1109/MCI.2006.329691 ErnstingSKuchenHData parallel algorithmic skeletons with accelerator supportInt. J. Parallel Prog.201745228329910.1007/s10766-016-0416-7 Lee, S.Y., Bau, Y.-T.: An ant colony optimization approach for solving the Multidimensional Knapsack Problem. In: 2012 International Conference on Computer & Information Science (ICCIS), pp. 441–446. IEEE (2012) Benoit, A., Cole, M., Gilmore, S., Hillston, J.: Flexible skeletal programming with eskel. In: European Conference on Parallel Processing, pp. 761–770. Springer, Berlin (2005) Menezes, B.A.D.M., Herrmann, N.: Musket repository. https://github.com/wwu-pi/musket_dsl (2020) Heidelberg University. Discrete and combinatorial optimization. https://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/XML-TSPLIB/instances/. Accessed 14 March 2018 Cole, M.I.: Algorithmic Skeletons: Structured Management of Parallel Computation. Pitman London (1989) Riguzzi, F.: A survey of software metrics. Technical report (1996) Dorigo, M., Caro, G.D.: Ant colony optimization: a new meta-heuristic (1999) FalkenauerEA hybrid grouping genetic algorithm for bin packingJ. Heuristics1996253010.1007/BF00226291 Menezes, B.A.D.M., Herrmann, N.: Ant colony optimization project. https://github.com/brenoamm/ant-colony-optimization-project (2021). Accessed 24 March 2021 Menezes, B.A., Kuchen, H., Neto, H.A.A., de Lima Neto, F.B.: Parallelization strategies for GPU-based ant colony optimization solving the traveling salesman problem. In: 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, pp. 3094–3101 (2019) Menezes, B.A.D.M., Pessoa, L.F.D.A., Kuchen, H., Neto, F.B.D.L.: Parallelization strategies for GPU- ased ant colony optimization applied to TSP. Adv. Parallel Comput., 36, 321–330 (2020) Beasley, J.E.: OR-Library: distributing test problems by electronic mail. J. Oper. Res. Soc. pp. 1069–1072 (1990) RiegerCWredeFKuchenHMusket: a domain-specific language for high-level parallel programming with algorithmic skeletonsProc. ACM Symp. Appl. Comput. Part2019F14777215341543 LevineJDucatelleFAnt colony optimization and local search for bin packing and cutting stock problemsJ. Oper. Res. Soc.200455770571610.1057/palgrave.jors.2601771 Dorigo, M.: Optimization, Learning and Natural Algorithms[in Italian]. PhD thesis, Dipartimentodi Elettronica, Politecnico di Milano, Milan (1992) Aldinucci, M., Danelutto, M., Kilpatrick, P., Torquati, M.: Fastflow: high-level and efficient streaming on multi-core. Programming Multi-Core and Many-Core Computing Systems, Parallel and Distributed Computing (2017) DelormeMIoriMMartelloSBin packing and cutting stock problems: mathematical models and exact algorithmsEur. J. Oper. Res.2016255120351378510.1016/j.ejor.2016.04.030 UchidaAItoYNakanoKAccelerating ant colony optimisation for the travelling salesman problem on the GPUInt. J. Parallel Emergent Distrib. Syst.201429440142010.1080/17445760.2013.842568 The Eclipse Foundation. Xtext documentation. https://eclipse.org/Xtext/documentation/ (2020) CeciliaJMGarcíaJMNisbetAAmosMUjaldónMEnhancing data parallelism for ant colony optimization on GPUsJ. Parallel Distrib. Comput.2013731425110.1016/j.jpdc.2012.01.002 Talbi, E-G.: Metaheuristics. Wiley, Hoboken, NJ (2009) Wrede, F., Rieger, C., Kuchen, H.: Generation of high-performance code based on a domain-specific language for algorithmic skeletons. J. Supercomput. 0123456789 (2019) ErnstingSKuchenHAlgorithmic skeletons for multi-core, multi-gpu systems and clustersInt. J. High Perform. Comput. Networking20127212913810.1504/IJHPCN.2012.046370 University of Waterloo. National traveling salesman problems. http://www.math.uwaterloo.ca/tsp/world/countries.html. Accessed 14 March 2018 Kallioras, N.A., Kepaptsoglou, K., Lagaros, N.D.: Transit stop inspection and maintenance scheduling: A GPU accelerated metaheuristics approach. Transp. Res. Part C Emerg. Technol., 55, 246–260 (2015) ÖhbergTErnstssonAKesslerCHybrid cpu-gpu execution support in the skeleton programming framework skepuJ. Supercomput.20207675038505610.1007/s11227-019-02824-7 S Ernsting (714_CR17) 2012; 7 714_CR15 714_CR19 M Dorigo (714_CR7) 2006; 1 714_CR8 714_CR10 A Uchida (714_CR11) 2014; 29 714_CR4 714_CR13 S Ernsting (714_CR18) 2017; 45 714_CR6 714_CR14 714_CR1 714_CR3 714_CR2 M Delorme (714_CR26) 2016; 255 J Levine (714_CR9) 2004; 55 JM Cecilia (714_CR12) 2013; 73 714_CR28 E Falkenauer (714_CR27) 1996; 2 714_CR20 714_CR21 714_CR22 T Öhberg (714_CR16) 2020; 76 714_CR23 C Rieger (714_CR5) 2019; F147772 714_CR24 714_CR25 |
| References_xml | – reference: LevineJDucatelleFAnt colony optimization and local search for bin packing and cutting stock problemsJ. Oper. Res. Soc.200455770571610.1057/palgrave.jors.2601771 – reference: University of Waterloo. National traveling salesman problems. http://www.math.uwaterloo.ca/tsp/world/countries.html. Accessed 14 March 2018 – reference: Menezes, B.A.D.M., Herrmann, N.: Ant colony optimization project. https://github.com/brenoamm/ant-colony-optimization-project (2021). Accessed 24 March 2021 – reference: The Eclipse Foundation. Xtext documentation. https://eclipse.org/Xtext/documentation/ (2020) – reference: Dorigo, M., Caro, G.D.: Ant colony optimization: a new meta-heuristic (1999) – reference: Lee, S.Y., Bau, Y.-T.: An ant colony optimization approach for solving the Multidimensional Knapsack Problem. In: 2012 International Conference on Computer & Information Science (ICCIS), pp. 441–446. IEEE (2012) – reference: DorigoMBirattariMStutzleTAnt colony optimizationIEEE Comput. Intell. Mag.200614283910.1109/MCI.2006.329691 – reference: Kallioras, N.A., Kepaptsoglou, K., Lagaros, N.D.: Transit stop inspection and maintenance scheduling: A GPU accelerated metaheuristics approach. Transp. Res. Part C Emerg. Technol., 55, 246–260 (2015) – reference: ErnstingSKuchenHData parallel algorithmic skeletons with accelerator supportInt. J. Parallel Prog.201745228329910.1007/s10766-016-0416-7 – reference: Heidelberg University. Discrete and combinatorial optimization. https://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/XML-TSPLIB/instances/. Accessed 14 March 2018 – reference: DelormeMIoriMMartelloSBin packing and cutting stock problems: mathematical models and exact algorithmsEur. J. Oper. Res.2016255120351378510.1016/j.ejor.2016.04.030 – reference: Talbi, E-G.: Metaheuristics. Wiley, Hoboken, NJ (2009) – reference: RiegerCWredeFKuchenHMusket: a domain-specific language for high-level parallel programming with algorithmic skeletonsProc. ACM Symp. Appl. Comput. Part2019F14777215341543 – reference: UchidaAItoYNakanoKAccelerating ant colony optimisation for the travelling salesman problem on the GPUInt. J. Parallel Emergent Distrib. Syst.201429440142010.1080/17445760.2013.842568 – reference: Benoit, A., Cole, M., Gilmore, S., Hillston, J.: Flexible skeletal programming with eskel. In: European Conference on Parallel Processing, pp. 761–770. Springer, Berlin (2005) – reference: Beasley, J.E.: OR-Library: distributing test problems by electronic mail. J. Oper. Res. Soc. pp. 1069–1072 (1990) – reference: Menezes, B.A., Kuchen, H., Neto, H.A.A., de Lima Neto, F.B.: Parallelization strategies for GPU-based ant colony optimization solving the traveling salesman problem. In: 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, pp. 3094–3101 (2019) – reference: Cole, M.I.: Algorithmic Skeletons: Structured Management of Parallel Computation. Pitman London (1989) – reference: ÖhbergTErnstssonAKesslerCHybrid cpu-gpu execution support in the skeleton programming framework skepuJ. Supercomput.20207675038505610.1007/s11227-019-02824-7 – reference: Menezes, B.A.D.M., Herrmann, N.: Musket repository. https://github.com/wwu-pi/musket_dsl (2020) – reference: Menezes, B.A.D.M., Pessoa, L.F.D.A., Kuchen, H., Neto, F.B.D.L.: Parallelization strategies for GPU- ased ant colony optimization applied to TSP. Adv. Parallel Comput., 36, 321–330 (2020) – reference: Aldinucci, M., Danelutto, M., Kilpatrick, P., Torquati, M.: Fastflow: high-level and efficient streaming on multi-core. Programming Multi-Core and Many-Core Computing Systems, Parallel and Distributed Computing (2017) – reference: Riguzzi, F.: A survey of software metrics. 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