Protein docking GPU acceleration
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/82980 |
Resumo: | In Bioinformatics, finding the complex resulting from an interaction between a pair of proteins is a computationally demanding task. There are methods and algorithms that simulate the binding between two proteins. However, the computation related to docking simulation has many extensive and repeating steps. Thus, the execution of the simulation if the program is CPU-only can last for hours, making the option of using these programs a very inefficient one in terms of work/time. One of the methods used is BiGGER, created by prof. Nuno Palma and others. This algorithm has features that give it a lower time complexity compared to others, therefore execution times in BiGGER can be lower than most of the algorithms fitted for execution of docking simulations. Studying protein interactions has medical aplications, contributing to the development of ways to protect, diagnose and heal humanity from neuronal diseases. It also contributes to computer assisted drug design and development. To improve the execution time of docking programs, these were optimized for GPU execution, reducing the execution time in docking scenarios that can last for hours to minutes or even seconds. This document presents an aproach to the implementation of optimizations to BiGGER, running it with GPU assistance. This implementation is to be done via high performance computing techniques, so that the machine’s GPU assists the CPU on parallelizing those required computations. By having more resources at disposal, it should be expected that the execution time of BiGGER is reduced due to the improvement of BiGGER performance in relation to the sequential version. If the implementations succeed, there will be additional advantages for BiGGER in relation to other algorithms. Thus, a value proposition is given for those who intend to use BiGGER as a method for efficiently studying interactions between proteins in a personal ou professional computer. |
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Protein docking GPU accelerationproteinsdockinghigh performance computingGPUBioinformaticsBiGGERDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaIn Bioinformatics, finding the complex resulting from an interaction between a pair of proteins is a computationally demanding task. There are methods and algorithms that simulate the binding between two proteins. However, the computation related to docking simulation has many extensive and repeating steps. Thus, the execution of the simulation if the program is CPU-only can last for hours, making the option of using these programs a very inefficient one in terms of work/time. One of the methods used is BiGGER, created by prof. Nuno Palma and others. This algorithm has features that give it a lower time complexity compared to others, therefore execution times in BiGGER can be lower than most of the algorithms fitted for execution of docking simulations. Studying protein interactions has medical aplications, contributing to the development of ways to protect, diagnose and heal humanity from neuronal diseases. It also contributes to computer assisted drug design and development. To improve the execution time of docking programs, these were optimized for GPU execution, reducing the execution time in docking scenarios that can last for hours to minutes or even seconds. This document presents an aproach to the implementation of optimizations to BiGGER, running it with GPU assistance. This implementation is to be done via high performance computing techniques, so that the machine’s GPU assists the CPU on parallelizing those required computations. By having more resources at disposal, it should be expected that the execution time of BiGGER is reduced due to the improvement of BiGGER performance in relation to the sequential version. If the implementations succeed, there will be additional advantages for BiGGER in relation to other algorithms. Thus, a value proposition is given for those who intend to use BiGGER as a method for efficiently studying interactions between proteins in a personal ou professional computer.Paulino, HervéKrippahl, LudwigRUNRibeiro, Ricardo Alexandre do Rosário2019-10-02T14:57:20Z2019-0720192019-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/82980porinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:36:55Zoai:run.unl.pt:10362/82980Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:36:15.706892Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Protein docking GPU acceleration |
title |
Protein docking GPU acceleration |
spellingShingle |
Protein docking GPU acceleration Ribeiro, Ricardo Alexandre do Rosário proteins docking high performance computing GPU Bioinformatics BiGGER Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
title_short |
Protein docking GPU acceleration |
title_full |
Protein docking GPU acceleration |
title_fullStr |
Protein docking GPU acceleration |
title_full_unstemmed |
Protein docking GPU acceleration |
title_sort |
Protein docking GPU acceleration |
author |
Ribeiro, Ricardo Alexandre do Rosário |
author_facet |
Ribeiro, Ricardo Alexandre do Rosário |
author_role |
author |
dc.contributor.none.fl_str_mv |
Paulino, Hervé Krippahl, Ludwig RUN |
dc.contributor.author.fl_str_mv |
Ribeiro, Ricardo Alexandre do Rosário |
dc.subject.por.fl_str_mv |
proteins docking high performance computing GPU Bioinformatics BiGGER Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
topic |
proteins docking high performance computing GPU Bioinformatics BiGGER Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
description |
In Bioinformatics, finding the complex resulting from an interaction between a pair of proteins is a computationally demanding task. There are methods and algorithms that simulate the binding between two proteins. However, the computation related to docking simulation has many extensive and repeating steps. Thus, the execution of the simulation if the program is CPU-only can last for hours, making the option of using these programs a very inefficient one in terms of work/time. One of the methods used is BiGGER, created by prof. Nuno Palma and others. This algorithm has features that give it a lower time complexity compared to others, therefore execution times in BiGGER can be lower than most of the algorithms fitted for execution of docking simulations. Studying protein interactions has medical aplications, contributing to the development of ways to protect, diagnose and heal humanity from neuronal diseases. It also contributes to computer assisted drug design and development. To improve the execution time of docking programs, these were optimized for GPU execution, reducing the execution time in docking scenarios that can last for hours to minutes or even seconds. This document presents an aproach to the implementation of optimizations to BiGGER, running it with GPU assistance. This implementation is to be done via high performance computing techniques, so that the machine’s GPU assists the CPU on parallelizing those required computations. By having more resources at disposal, it should be expected that the execution time of BiGGER is reduced due to the improvement of BiGGER performance in relation to the sequential version. If the implementations succeed, there will be additional advantages for BiGGER in relation to other algorithms. Thus, a value proposition is given for those who intend to use BiGGER as a method for efficiently studying interactions between proteins in a personal ou professional computer. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-02T14:57:20Z 2019-07 2019 2019-07-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/82980 |
url |
http://hdl.handle.net/10362/82980 |
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por |
language |
por |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799137981804576768 |