Enhancing GPU parallelism in nature-inspired algorithms
We present GPU implementations of two different nature-inspired optimization methods for well-known optimization problems. Ant Colony Optimization (ACO) is a two-stage population-based method modelled on the foraging behaviour of ants, while P systems provide a high-level computational modelling fra...
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| Published in | The Journal of supercomputing Vol. 63; no. 3; pp. 773 - 789 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.03.2013
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0920-8542 1573-0484 |
| DOI | 10.1007/s11227-012-0770-1 |
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| Abstract | We present GPU implementations of two different nature-inspired optimization methods for well-known optimization problems. Ant Colony Optimization (ACO) is a two-stage population-based method modelled on the foraging behaviour of ants, while P systems provide a high-level computational modelling framework that combines the structure and dynamic aspects of biological systems (in particular, their parallel and non-deterministic nature). Our methods focus on exploiting data parallelism and memory hierarchy to obtain GPU factor gains surpassing 20x for any of the two stages of the ACO algorithm, and 16x for P systems when compared to sequential versions running on a single-threaded high-end CPU. Additionally, we compare performance between GPU generations to validate hardware enhancements introduced by Nvidia’s Fermi architecture. |
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| AbstractList | We present GPU implementations of two different nature-inspired optimization methods for well-known optimization problems. Ant Colony Optimization (ACO) is a two-stage population-based method modelled on the foraging behaviour of ants, while P systems provide a high-level computational modelling framework that combines the structure and dynamic aspects of biological systems (in particular, their parallel and non-deterministic nature). Our methods focus on exploiting data parallelism and memory hierarchy to obtain GPU factor gains surpassing 20x for any of the two stages of the ACO algorithm, and 16x for P systems when compared to sequential versions running on a single-threaded high-end CPU. Additionally, we compare performance between GPU generations to validate hardware enhancements introduced by Nvidiaas Fermi architecture. We present GPU implementations of two different nature-inspired optimization methods for well-known optimization problems. Ant Colony Optimization (ACO) is a two-stage population-based method modelled on the foraging behaviour of ants, while P systems provide a high-level computational modelling framework that combines the structure and dynamic aspects of biological systems (in particular, their parallel and non-deterministic nature). Our methods focus on exploiting data parallelism and memory hierarchy to obtain GPU factor gains surpassing 20x for any of the two stages of the ACO algorithm, and 16x for P systems when compared to sequential versions running on a single-threaded high-end CPU. Additionally, we compare performance between GPU generations to validate hardware enhancements introduced by Nvidia’s Fermi architecture. |
| Author | Ujaldón, Manuel García, José M. Nisbet, Andy Amos, Martyn Cecilia, José M. |
| Author_xml | – sequence: 1 givenname: José M. surname: Cecilia fullname: Cecilia, José M. email: jmcecilia@ucam.edu organization: Departamento de Informática, Escuela politécnica, Universidad Católica San Antonio Murcia – sequence: 2 givenname: Andy surname: Nisbet fullname: Nisbet, Andy organization: School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University – sequence: 3 givenname: Martyn surname: Amos fullname: Amos, Martyn organization: School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University – sequence: 4 givenname: José M. surname: García fullname: García, José M. organization: Facultad de Informática, Universidad de Murcia – sequence: 5 givenname: Manuel surname: Ujaldón fullname: Ujaldón, Manuel organization: Computer Architecture Department, ETSI Informática, University of Málaga |
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| Cites_doi | 10.1023/A:1025449224520 10.1287/ijoc.3.4.376 10.1006/jcss.1999.1693 10.1145/1839676.1839694 10.1109/MM.2008.57 10.1016/j.plrev.2005.10.001 10.1016/S0167-739X(00)00042-X 10.1109/3477.484436 10.1093/bib/bbp064 10.1007/978-3-642-56196-2 10.1007/b99492 10.1145/106973.106981 10.1109/CI-M.2006.248054 |
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| Keywords | Bioinspired methods HPC ACO GPUs P systems |
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| References | Cecilia, García, Guerrero, Martínez-del-Amor, Pérez-Hurtado, Pérez-Jiménez (CR2) 2010; 11 Fu, Lei, Zhou (CR11) 2010 Dorigo, Maniezzo, Colorni (CR9) 1996; 26 Díaz-Pernil, Pérez-Hurtado, Pérez-Jiménez, Riscos-Núñez (CR4) 2008 Dorigo, Stützle (CR10) 2004 CR19 Păun (CR22) 2002 Cecilia, García, Guerrero, Martínez-del-Amor, Pérez-Jiménez, Ujaldón (CR3) 2010 Lam, Rothberg, Wolf (CR16) 1991; 26 Blum (CR1) 2005; 2 Lawler, Lenstra, Kan, Shmoys (CR17) 1987 Reinelt (CR26) 1991; 3 Goldberg (CR14) 1989 Păun (CR23) 2000; 61 Johnson, Mcgeoch, Aarts, Lenstra (CR15) 1997 You (CR29) 2009 Li, Hu, Pang, Qian (CR18) 2009; 5 Dorigo, Birattari, Stützle (CR6) 2006; 1 CR5 CR8 Pérez-Jiménez, Romero-Jiménez, Sancho-Caparrini (CR24) 2003; 2 CR28 CR27 CR25 CR21 CR20 Garland, Le Grand, Nickolls (CR12) 2008; 28 Garland, Kirk (CR13) 2010; 53 Dorigo, Bonabeau, Theraulaz (CR7) 2000; 16 M Garland (770_CR12) 2008; 28 YS You (770_CR29) 2009 M Garland (770_CR13) 2010; 53 MJ Pérez-Jiménez (770_CR24) 2003; 2 G Păun (770_CR23) 2000; 61 G Reinelt (770_CR26) 1991; 3 M Dorigo (770_CR9) 1996; 26 M Dorigo (770_CR7) 2000; 16 770_CR5 MD Lam (770_CR16) 1991; 26 M Dorigo (770_CR6) 2006; 1 770_CR27 770_CR8 G Păun (770_CR22) 2002 770_CR25 770_CR28 JM Cecilia (770_CR2) 2010; 11 E Lawler (770_CR17) 1987 J Fu (770_CR11) 2010 770_CR20 770_CR21 DS Johnson (770_CR15) 1997 J Li (770_CR18) 2009; 5 DE Goldberg (770_CR14) 1989 JM Cecilia (770_CR3) 2010 D Díaz-Pernil (770_CR4) 2008 M Dorigo (770_CR10) 2004 C Blum (770_CR1) 2005; 2 770_CR19 |
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| Title | Enhancing GPU parallelism in nature-inspired algorithms |
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