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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

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Dourado, Jonas R. [1] ; de Oliveira Junior, Jordao Natal [1] ; Maciel, Carlos D. [1]
Total Authors: 3
[1] Univ Sao Paulo, Dept Elect & Computat Engn, BR-13566590 Sao Carlos, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: ALGORITHMS; v. 12, n. 9 SEP 2019.
Web of Science Citations: 1

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)

FAPESP's process: 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment
Grantee:Marco Henrique Terra
Support type: Research Projects - Thematic Grants
FAPESP's process: 18/19150-6 - Resilience of complex systems with the use of dynamic Bayesian Networks: a probabilistic approach
Grantee:Carlos Dias Maciel
Support type: Scholarships abroad - Research