논문 인용하기
각 논문마다 생성되어 있는 BibTeX를 사용하시면 자신이 원하는 스타일의 인용 문구를 생성할 수 있습니다.
생성된 BibTeX 코드를 복사하여 BibTeX Parser를 사용해 일반 문자열로 바꾸십시오. 아래의 사이트와 같이 웹에서 변환할 수도 있습니다.
bibtex.online2021
1.
Yoon, Daegun; Oh, Sangyoon
Traversing Large Road Networks on GPUs with Breadth-First Search🌏 InternationalConference 📃 In press
The 7th International Conference on Next Generation Computing, 2021.
Abstract | BibTeX | 태그: breath-first search, graph, graphics processing units, road network
@conference{Yoon2021Traversing,
title = {Traversing Large Road Networks on GPUs with Breadth-First Search},
author = {Daegun Yoon and Sangyoon Oh},
year = {2021},
date = {2021-11-05},
urldate = {2021-11-05},
booktitle = {The 7th International Conference on Next Generation Computing},
journal = {The 7th International Conference on Next Generation Computing 2021},
abstract = {Breadth-first search (BFS) is one of the most used graph kernels, and substantially affects the overall performance when processing various graphs. Since graph data are frequently used in real life for example road networks in navigation systems, high performance graph processing becomes more critical. In this study, we aim to process BFS algorithm efficiently on road network data. We propose BARON, a BFS algorithm that copes with road networks. To accelerate graph traversal, BARON reduce the occurrence of branch and memory divergences by exploiting warp-cooperative work sharing and atomic operations. With this design approach, BARON outperforms the other BFS kernels of state-of-the-art graph processing frameworks executed stably on the latest GPU architectures. For various graphs, BARON yields speedups of up to 2.88x and 5.43x over Gunrock and CuSha, respectively.},
keywords = {breath-first search, graph, graphics processing units, road network},
pubstate = {published},
tppubtype = {conference}
}
Breadth-first search (BFS) is one of the most used graph kernels, and substantially affects the overall performance when processing various graphs. Since graph data are frequently used in real life for example road networks in navigation systems, high performance graph processing becomes more critical. In this study, we aim to process BFS algorithm efficiently on road network data. We propose BARON, a BFS algorithm that copes with road networks. To accelerate graph traversal, BARON reduce the occurrence of branch and memory divergences by exploiting warp-cooperative work sharing and atomic operations. With this design approach, BARON outperforms the other BFS kernels of state-of-the-art graph processing frameworks executed stably on the latest GPU architectures. For various graphs, BARON yields speedups of up to 2.88x and 5.43x over Gunrock and CuSha, respectively.