Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems

Abstract This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhu, Weihang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Differential evolution Pattern search Graphics hardware acceleration GPU CUDA

Übergeordnetes Werk:	Enthalten in: Journal of global optimization - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 50(2010), 3 vom: 15. Aug., Seite 417-437
Übergeordnetes Werk:	volume:50 ; year:2010 ; number:3 ; day:15 ; month:08 ; pages:417-437

Links:	Volltext

DOI / URN:	10.1007/s10898-010-9590-0

Katalog-ID:	SPR01445260X

Internformat


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520			\|a Abstract This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE–PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer.
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matchkey_str	article:15732916:2010----::asvlprledfeetaeouinatrsacotmztowtgahchrwraclrtoaivsiain
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publishDate	2010
allfields	10.1007/s10898-010-9590-0 doi (DE-627)SPR01445260X (SPR)s10898-010-9590-0-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl 83.00 bkl 85.00 bkl Zhu, Weihang verfasserin aut Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE–PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer. Differential evolution (dpeaa)DE-He213 Pattern search (dpeaa)DE-He213 Graphics hardware acceleration (dpeaa)DE-He213 GPU (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Enthalten in Journal of global optimization Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 50(2010), 3 vom: 15. Aug., Seite 417-437 (DE-627)266882196 (DE-600)1468063-4 1573-2916 nnns volume:50 year:2010 number:3 day:15 month:08 pages:417-437 https://dx.doi.org/10.1007/s10898-010-9590-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE 83.00 ASE 85.00 ASE AR 50 2010 3 15 08 417-437
spelling	10.1007/s10898-010-9590-0 doi (DE-627)SPR01445260X (SPR)s10898-010-9590-0-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl 83.00 bkl 85.00 bkl Zhu, Weihang verfasserin aut Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE–PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer. Differential evolution (dpeaa)DE-He213 Pattern search (dpeaa)DE-He213 Graphics hardware acceleration (dpeaa)DE-He213 GPU (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Enthalten in Journal of global optimization Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 50(2010), 3 vom: 15. Aug., Seite 417-437 (DE-627)266882196 (DE-600)1468063-4 1573-2916 nnns volume:50 year:2010 number:3 day:15 month:08 pages:417-437 https://dx.doi.org/10.1007/s10898-010-9590-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE 83.00 ASE 85.00 ASE AR 50 2010 3 15 08 417-437
allfields_unstemmed	10.1007/s10898-010-9590-0 doi (DE-627)SPR01445260X (SPR)s10898-010-9590-0-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl 83.00 bkl 85.00 bkl Zhu, Weihang verfasserin aut Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE–PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer. Differential evolution (dpeaa)DE-He213 Pattern search (dpeaa)DE-He213 Graphics hardware acceleration (dpeaa)DE-He213 GPU (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Enthalten in Journal of global optimization Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 50(2010), 3 vom: 15. Aug., Seite 417-437 (DE-627)266882196 (DE-600)1468063-4 1573-2916 nnns volume:50 year:2010 number:3 day:15 month:08 pages:417-437 https://dx.doi.org/10.1007/s10898-010-9590-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE 83.00 ASE 85.00 ASE AR 50 2010 3 15 08 417-437
allfieldsGer	10.1007/s10898-010-9590-0 doi (DE-627)SPR01445260X (SPR)s10898-010-9590-0-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl 83.00 bkl 85.00 bkl Zhu, Weihang verfasserin aut Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE–PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer. Differential evolution (dpeaa)DE-He213 Pattern search (dpeaa)DE-He213 Graphics hardware acceleration (dpeaa)DE-He213 GPU (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Enthalten in Journal of global optimization Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 50(2010), 3 vom: 15. Aug., Seite 417-437 (DE-627)266882196 (DE-600)1468063-4 1573-2916 nnns volume:50 year:2010 number:3 day:15 month:08 pages:417-437 https://dx.doi.org/10.1007/s10898-010-9590-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE 83.00 ASE 85.00 ASE AR 50 2010 3 15 08 417-437
allfieldsSound	10.1007/s10898-010-9590-0 doi (DE-627)SPR01445260X (SPR)s10898-010-9590-0-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl 83.00 bkl 85.00 bkl Zhu, Weihang verfasserin aut Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE–PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer. Differential evolution (dpeaa)DE-He213 Pattern search (dpeaa)DE-He213 Graphics hardware acceleration (dpeaa)DE-He213 GPU (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Enthalten in Journal of global optimization Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 50(2010), 3 vom: 15. Aug., Seite 417-437 (DE-627)266882196 (DE-600)1468063-4 1573-2916 nnns volume:50 year:2010 number:3 day:15 month:08 pages:417-437 https://dx.doi.org/10.1007/s10898-010-9590-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE 83.00 ASE 85.00 ASE AR 50 2010 3 15 08 417-437
language	English
source	Enthalten in Journal of global optimization 50(2010), 3 vom: 15. Aug., Seite 417-437 volume:50 year:2010 number:3 day:15 month:08 pages:417-437
sourceStr	Enthalten in Journal of global optimization 50(2010), 3 vom: 15. Aug., Seite 417-437 volume:50 year:2010 number:3 day:15 month:08 pages:417-437
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Differential evolution Pattern search Graphics hardware acceleration GPU CUDA
dewey-raw	510
isfreeaccess_bool	false
container_title	Journal of global optimization
authorswithroles_txt_mv	Zhu, Weihang @@aut@@
publishDateDaySort_date	2010-08-15T00:00:00Z
hierarchy_top_id	266882196
dewey-sort	3510
id	SPR01445260X
language_de	englisch
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As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. 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author	Zhu, Weihang
spellingShingle	Zhu, Weihang ddc 510 bkl 31.00 bkl 83.00 bkl 85.00 misc Differential evolution misc Pattern search misc Graphics hardware acceleration misc GPU misc CUDA Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems
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topic_title	510 ASE 31.00 bkl 83.00 bkl 85.00 bkl Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems Differential evolution (dpeaa)DE-He213 Pattern search (dpeaa)DE-He213 Graphics hardware acceleration (dpeaa)DE-He213 GPU (dpeaa)DE-He213 CUDA (dpeaa)DE-He213
topic	ddc 510 bkl 31.00 bkl 83.00 bkl 85.00 misc Differential evolution misc Pattern search misc Graphics hardware acceleration misc GPU misc CUDA
topic_unstemmed	ddc 510 bkl 31.00 bkl 83.00 bkl 85.00 misc Differential evolution misc Pattern search misc Graphics hardware acceleration misc GPU misc CUDA
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title	Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems
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title_full	Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems
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title_sort	massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems
title_auth	Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems
abstract	Abstract This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE–PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer.
abstractGer	Abstract This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE–PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer.
abstract_unstemmed	Abstract This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE–PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer.
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title_short	Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems
url	https://dx.doi.org/10.1007/s10898-010-9590-0
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doi_str	10.1007/s10898-010-9590-0
up_date	2024-07-04T01:47:25.695Z
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Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems

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