Geração de horários usando GPUs

Detalhes bibliográficos
Autor(a) principal: Ricardo Filipe Pereira Neves
Data de Publicação: 2016
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/85408
Resumo: Throughout an academic year, educational institutions need to generate hundreds of different timetables, this complex task demands a considerable amount of time and human resources. In the past, timetable generation was handmade, in current days as this task complexity increases, it is performed by specialized software which allows to reduce time and costs. Since nearly 10 years ago, single core performance has stopped because it became unfeasible for manufacturers due heat generation and power consumption, in order to achieve higher performance each core frequency was reduced while number of cores were increased. A GPU, is a very capable piece of hardware, it has two particular strengths: memory bandwidth (GB/sec) and raw power (GFLOPS), most of the complex and difficult computational problems fall into these categories and timetable generation problem is no exception. Timetable generation software doesn't always takes advantage of hardware capabilities to perform parallel computations, this dissertation aims to explore GPU's capabilities in order to prove the concept of one or more possible parallel simplified implementations. It is expected that using GPU's to solve this problem the amount of computation time would be reduced substantially compared with CPU implementations in single or multithread.
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spelling Geração de horários usando GPUsEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringThroughout an academic year, educational institutions need to generate hundreds of different timetables, this complex task demands a considerable amount of time and human resources. In the past, timetable generation was handmade, in current days as this task complexity increases, it is performed by specialized software which allows to reduce time and costs. Since nearly 10 years ago, single core performance has stopped because it became unfeasible for manufacturers due heat generation and power consumption, in order to achieve higher performance each core frequency was reduced while number of cores were increased. A GPU, is a very capable piece of hardware, it has two particular strengths: memory bandwidth (GB/sec) and raw power (GFLOPS), most of the complex and difficult computational problems fall into these categories and timetable generation problem is no exception. Timetable generation software doesn't always takes advantage of hardware capabilities to perform parallel computations, this dissertation aims to explore GPU's capabilities in order to prove the concept of one or more possible parallel simplified implementations. It is expected that using GPU's to solve this problem the amount of computation time would be reduced substantially compared with CPU implementations in single or multithread.2016-07-112016-07-11T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/85408TID:201318091engRicardo Filipe Pereira Nevesinfo: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:RCAAP2023-11-29T13:21:36Zoai:repositorio-aberto.up.pt:10216/85408Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:39:08.110908Repositório 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 Geração de horários usando GPUs
title Geração de horários usando GPUs
spellingShingle Geração de horários usando GPUs
Ricardo Filipe Pereira Neves
Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
title_short Geração de horários usando GPUs
title_full Geração de horários usando GPUs
title_fullStr Geração de horários usando GPUs
title_full_unstemmed Geração de horários usando GPUs
title_sort Geração de horários usando GPUs
author Ricardo Filipe Pereira Neves
author_facet Ricardo Filipe Pereira Neves
author_role author
dc.contributor.author.fl_str_mv Ricardo Filipe Pereira Neves
dc.subject.por.fl_str_mv Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
topic Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
description Throughout an academic year, educational institutions need to generate hundreds of different timetables, this complex task demands a considerable amount of time and human resources. In the past, timetable generation was handmade, in current days as this task complexity increases, it is performed by specialized software which allows to reduce time and costs. Since nearly 10 years ago, single core performance has stopped because it became unfeasible for manufacturers due heat generation and power consumption, in order to achieve higher performance each core frequency was reduced while number of cores were increased. A GPU, is a very capable piece of hardware, it has two particular strengths: memory bandwidth (GB/sec) and raw power (GFLOPS), most of the complex and difficult computational problems fall into these categories and timetable generation problem is no exception. Timetable generation software doesn't always takes advantage of hardware capabilities to perform parallel computations, this dissertation aims to explore GPU's capabilities in order to prove the concept of one or more possible parallel simplified implementations. It is expected that using GPU's to solve this problem the amount of computation time would be reduced substantially compared with CPU implementations in single or multithread.
publishDate 2016
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