Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

Autor(es)

Dourado, Jonas R. ^[1] ; de Oliveira Junior, Jordao Natal ^[1] ; Maciel, Carlos D. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Dept Elect & Computat Engn, BR-13566590 Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: ALGORITHMS; v. 12, n. 9 SEP 2019.
• Citações Web of Science: 1

Resumo

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. (AU)

• Processo FAPESP: 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente
• Beneficiário: Marco Henrique Terra
• Linha de fomento: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/19150-6 - Resiliência de sistemas complexos com o uso de redes bayesianas dinâmicas: uma abordagem probabilística
• Beneficiário: Carlos Dias Maciel
• Linha de fomento: Bolsas no Exterior - Pesquisa