@inproceedings{0f6676b4112b417ba70c97456553d691,
title = "PyIF: A Fast and Light Weight Implementation to Estimate Bivariate Transfer Entropy for Big Data",
abstract = "Transfer entropy is an information measure that quantifies information flow between processes evolving in time. Transfer entropy has a plethora of potential applications in financial markets, canonical systems, neuroscience, and social media. We offer a fast open source Python implementation called PyIF that estimates Transfer Entropy with Kraskov's method. PyIF utilizes KD-Trees, multiple processes by parallelizing queries on said KD-Trees, and can be used with CUDA compatible GPUs to significantly reduce the wall time for estimating transfer entropy. We find from our analyses that PyIF's GPU implementation is up to 1072 times faster (and it's CPU implementation is up 181 times faster) than existing implementations to estimate transfer entropy on large data and scales better than existing implementatin. ",
keywords = "Parallel Processing, Transfer Entropy",
author = "Ikegwu, {Kelechi M.} and Jacob Trauger and Jeff McMullin and Brunner, {Robert J.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE SoutheastCon, SoutheastCon 2020 ; Conference date: 28-03-2020 Through 29-03-2020",
year = "2020",
month = mar,
day = "28",
doi = "10.1109/SoutheastCon44009.2020.9249650",
language = "English (US)",
series = "Conference Proceedings - IEEE SOUTHEASTCON",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE SoutheastCon 2020, SoutheastCon 2020",
address = "United States",
}