Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence.
Dedico este trabalho a minha noiva, meus irmãos e pais que sempre estiveram ao meu lado. Amo todos: Beatriz, Amália, Breno, Sarah, Ângela e Marcos Dedico também aos meus Avós que muito me ensinaram sobre a vida. E a todos que eu gostaria de passar mais tempo junto.
AGRADECIMENTOSAgradeço antes de mais nada a Deus.Agradeço meu amigo e orientador Carlos Dias Maciel por sempre estar disposto a ajudar.Agradeço aos meus colegas de laboratório . As discussões científicas do dia a dia contribuiram para o meu desenvolvimento científico.Aos meus amigos André Andrew Cunha, Bruno Brubru Begotti, Leandro Gaspar e Ivan Filgueiras que sempre incentivaram minha curiosidade.Por último e não menos importante, agradeço a todos os meus amigos de José Bonifácio pela amizade, fazendo de lá, a melhor cidade de todas! Não tentarei listarei todos pois tenho certeza que um ou outro ficaria de fora injustamente."They did not know it was impossible so they did it" Mark Twain ABSTRACT DOURADO, J. Delayed Transfer Entropy applied to Big Data. 2018. 97p.
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