@inproceedings{1fab13d058ec488b9bd3f53bb4e2a0e1,
title = "Optimizing energy consumption and parallel performance for static and dynamic betweenness centrality using GPUs",
abstract = "Applications of high-performance graph analysis range from computational biology to network security and even transportation. These applications often consider graphs under rapid change and are moving beyond HPC platforms into energy-constrained embedded systems. This paper optimizes one successful and demanding analysis kernel, betweenness centrality, for NVIDIA GPU accelerators in both environments. Our algorithm for static analysis is capable of exceeding 2 million traversed edges per second per watt (MTEPS/W). Optimizing the parallel algorithm and treating the dynamic problem directly achieves a 6.9× average speed-up and 83% average reduction in energy consumption.",
author = "Adam McLaughlin and Jason Riedy and Bader, {David A.}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE High Performance Extreme Computing Conference, HPEC 2014 ; Conference date: 09-09-2014 Through 11-09-2014",
year = "2014",
month = feb,
day = "11",
doi = "10.1109/HPEC.2014.7040980",
language = "English (US)",
series = "2014 IEEE High Performance Extreme Computing Conference, HPEC 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2014 IEEE High Performance Extreme Computing Conference, HPEC 2014",
address = "United States",
}