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

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 progra...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gmys, Jan [verfasserIn] Carneiro, Tiago [verfasserIn] Melab, Nouredine [verfasserIn] Talbi, El-Ghazali [verfasserIn] Tuyttens, Daniel [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Metaheuristics Parallel metaheuristics High-productivity languages Parallel computing

Übergeordnetes Werk:	Enthalten in: Swarm and evolutionary computation - Amsterdam [u.a.] : Elsevier, 2011, 57
Übergeordnetes Werk:	volume:57

DOI / URN:	10.1016/j.swevo.2020.100720

Katalog-ID:	ELV00453302X

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520			\|a 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.
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650		4	\|a Parallel computing
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Indexfelder

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matchkey_str	gmysjancarneirotiagomelabnouredinetalbie:2020----:cmaaietdohgpoutvthgpromnergamnlnug
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publishDate	2020
allfields	10.1016/j.swevo.2020.100720 doi (DE-627)ELV00453302X (ELSEVIER)S2210-6502(20)30373-4 DE-627 ger DE-627 rda eng 004 DE-600 Gmys, Jan verfasserin aut A comparative study of high-productivity high-performance programming languages for parallel metaheuristics 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Metaheuristics Parallel metaheuristics High-productivity languages Parallel computing Carneiro, Tiago verfasserin aut Melab, Nouredine verfasserin aut Talbi, El-Ghazali verfasserin (orcid)0000-0003-4549-1010 aut Tuyttens, Daniel verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 57 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:57 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 57
spelling	10.1016/j.swevo.2020.100720 doi (DE-627)ELV00453302X (ELSEVIER)S2210-6502(20)30373-4 DE-627 ger DE-627 rda eng 004 DE-600 Gmys, Jan verfasserin aut A comparative study of high-productivity high-performance programming languages for parallel metaheuristics 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Metaheuristics Parallel metaheuristics High-productivity languages Parallel computing Carneiro, Tiago verfasserin aut Melab, Nouredine verfasserin aut Talbi, El-Ghazali verfasserin (orcid)0000-0003-4549-1010 aut Tuyttens, Daniel verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 57 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:57 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 57
allfields_unstemmed	10.1016/j.swevo.2020.100720 doi (DE-627)ELV00453302X (ELSEVIER)S2210-6502(20)30373-4 DE-627 ger DE-627 rda eng 004 DE-600 Gmys, Jan verfasserin aut A comparative study of high-productivity high-performance programming languages for parallel metaheuristics 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Metaheuristics Parallel metaheuristics High-productivity languages Parallel computing Carneiro, Tiago verfasserin aut Melab, Nouredine verfasserin aut Talbi, El-Ghazali verfasserin (orcid)0000-0003-4549-1010 aut Tuyttens, Daniel verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 57 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:57 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 57
allfieldsGer	10.1016/j.swevo.2020.100720 doi (DE-627)ELV00453302X (ELSEVIER)S2210-6502(20)30373-4 DE-627 ger DE-627 rda eng 004 DE-600 Gmys, Jan verfasserin aut A comparative study of high-productivity high-performance programming languages for parallel metaheuristics 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Metaheuristics Parallel metaheuristics High-productivity languages Parallel computing Carneiro, Tiago verfasserin aut Melab, Nouredine verfasserin aut Talbi, El-Ghazali verfasserin (orcid)0000-0003-4549-1010 aut Tuyttens, Daniel verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 57 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:57 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 57
allfieldsSound	10.1016/j.swevo.2020.100720 doi (DE-627)ELV00453302X (ELSEVIER)S2210-6502(20)30373-4 DE-627 ger DE-627 rda eng 004 DE-600 Gmys, Jan verfasserin aut A comparative study of high-productivity high-performance programming languages for parallel metaheuristics 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Metaheuristics Parallel metaheuristics High-productivity languages Parallel computing Carneiro, Tiago verfasserin aut Melab, Nouredine verfasserin aut Talbi, El-Ghazali verfasserin (orcid)0000-0003-4549-1010 aut Tuyttens, Daniel verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 57 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:57 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 57
language	English
source	Enthalten in Swarm and evolutionary computation 57 volume:57
sourceStr	Enthalten in Swarm and evolutionary computation 57 volume:57
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Metaheuristics Parallel metaheuristics High-productivity languages Parallel computing
dewey-raw	004
isfreeaccess_bool	false
container_title	Swarm and evolutionary computation
authorswithroles_txt_mv	Gmys, Jan @@aut@@ Carneiro, Tiago @@aut@@ Melab, Nouredine @@aut@@ Talbi, El-Ghazali @@aut@@ Tuyttens, Daniel @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	661267121
dewey-sort	14
id	ELV00453302X
language_de	englisch
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author	Gmys, Jan
spellingShingle	Gmys, Jan ddc 004 misc Metaheuristics misc Parallel metaheuristics misc High-productivity languages misc Parallel computing A comparative study of high-productivity high-performance programming languages for parallel metaheuristics
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topic_title	004 DE-600 A comparative study of high-productivity high-performance programming languages for parallel metaheuristics Metaheuristics Parallel metaheuristics High-productivity languages Parallel computing
topic	ddc 004 misc Metaheuristics misc Parallel metaheuristics misc High-productivity languages misc Parallel computing
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title	A comparative study of high-productivity high-performance programming languages for parallel metaheuristics
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title_sort	a comparative study of high-productivity high-performance programming languages for parallel metaheuristics
title_auth	A comparative study of high-productivity high-performance programming languages for parallel metaheuristics
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	A comparative study of high-productivity high-performance programming languages for parallel metaheuristics
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author2	Carneiro, Tiago Melab, Nouredine Talbi, El-Ghazali Tuyttens, Daniel
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A comparative study of high-productivity high-performance programming languages for parallel metaheuristics

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