PARALLELISM STRATEGIES FOR NEUROPHYSIOLOGICAL DELAYED TRANSFER ENTROPY DATA PROCESSING: TOWARDS CAUSAL INFERENCE IN BIG DATA

Abstract

Nowadays, the amount of data being generated and collected has been rising with the popularization of technologies such as Internet of Things, social media, and smartphone. The increasing amount of data led the creation of the term big data. One class of Big Data hidden information is causality. Among the tools to infer causal relationships there is Delayed 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 approach is to compare different parallel strategies to calculate DTE from neurophysiological time series using a heterogeneous Beowulf cluster aiming to increase DTE performance.