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Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

Department of Electrical and Computational Engineering, University of São Paulo, São Carlos-SP 13566-590, Brazil
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Algorithms 2019, 12(9), 190; https://doi.org/10.3390/a12090190
Received: 4 August 2019 / Revised: 31 August 2019 / Accepted: 3 September 2019 / Published: 9 September 2019
(This article belongs to the Special Issue Algorithms for Large Scale Data Analysis)
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.
Keywords: delayed transfer entropy; parallelism strategies; big data analysis; heterogeneous computer cluster; complex systems; causality; surrogate delayed transfer entropy; parallelism strategies; big data analysis; heterogeneous computer cluster; complex systems; causality; surrogate
MDPI and ACS Style

Dourado, J.R.; Júnior, J.N.O.; Maciel, C.D. Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation. Algorithms 2019, 12, 190.

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