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https://www.arca.fiocruz.br/handle/icict/15995
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Embargo date
2030-01-01
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- IOC - Artigos de Periódicos [12500]
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MANY TASK COMPUTING FOR ORTHOLOGOUS GENES IDENTIFICATION IN PROTOZOAN GENOMES USING HYDRA
Bioinformática
Many Task Computing scenarios
Hydra
orthologous genes identification
protozoan genomes
Author
Affilliation
Universidade Federal do Rio de Janeiro. COPPE. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. COPPE. Rio de Janeiro, RJ, Brasil / Centro Federal de Educação Tecnológica. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. COPPE. Rio de Janeiro, RJ, Brasil.
Universidade Federal Fluminense. Niterói, RJ, Brasil.
Universidade Federal do Rio de Janeiro. COPPE. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Pólo de Biologia Computacional e Sistemas. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. COPPE. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. COPPE. Rio de Janeiro, RJ, Brasil / Centro Federal de Educação Tecnológica. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. COPPE. Rio de Janeiro, RJ, Brasil.
Universidade Federal Fluminense. Niterói, RJ, Brasil.
Universidade Federal do Rio de Janeiro. COPPE. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Pólo de Biologia Computacional e Sistemas. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. COPPE. Rio de Janeiro, RJ, Brasil.
Abstract
One of the main advantages of using a scientific workflow management system (SWfMS) is to orchestrate
data flows among scientific activities and register provenance of the whole workflow execution. Nevertheless,
the execution control of distributed activities in high performance computing environments by SWfMS
presents challenges such as steering control and provenance gathering. Such challenges may become a
complex task to be accomplished in bioinformatics experiments, particularly in Many Task Computing scenarios.
This paper presents a data parallelism solution for a bioinformatics experiment supported by Hydra,
a middleware that bridges SWfMS and high performance computing to enable workflow parallelization with
provenance gathering. Hydra Many Task Computing parallelization strategies can be registered and reused.
Using Hydra, provenance may also be uniformly gathered. We have evaluated Hydra using an Orthologous
Gene Identification workflow. Experimental results show that a systematic approach for distributing parallel
activities is viable, sparing scientist time and diminishing operational errors, with the additional benefits of
distributed provenance support.
Keywords in Portuguese
Sistema de Gerenciamento de Fluxo de trabalho científico (WfMS)Bioinformática
Keywords
Scientific workflow management system (SWfMS)Many Task Computing scenarios
Hydra
orthologous genes identification
protozoan genomes
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