@inproceedings{5469e0419c3b456b9712dc899ae6d217,
title = "Hornet: An Efficient Data Structure for Dynamic Sparse Graphs and Matrices on GPUs",
abstract = "Sparse data computations are ubiquitous in science and engineering. Unlike their dense data counterparts, sparse data computations have less locality and more irregularity in their execution, making them significantly more challenging to parallelize and optimize. Many of the existing formats for sparse data representations on parallel architectures are restricted to static data problems, while those for dynamic data suffer from inefficiency both in terms of performance and memory footprint. This work presents Hornet, a novel data representation that targets dynamic data problems. Hornet is scalable with the input size, and does not require any data re-allocation or re-initialization during the data evolution. We show a Hornet implementation for GPU architectures and compare it to the most widely used static and dynamic data structures.",
keywords = "Dynamic Graph Structures, GPU Computing, Graph Analytics",
author = "Federico Busato and Oded Green and Nicola Bombieri and Bader, {David A.}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE High Performance Extreme Computing Conference, HPEC 2018 ; Conference date: 25-09-2018 Through 27-09-2018",
year = "2018",
month = nov,
day = "26",
doi = "10.1109/HPEC.2018.8547541",
language = "English (US)",
series = "2018 IEEE High Performance Extreme Computing Conference, HPEC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE High Performance Extreme Computing Conference, HPEC 2018",
address = "United States",
}