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.online2022
1.
Yoon, Daegun; Oh, Sangyoon
SURF: Direction-Optimizing Breadth-First Search Using Workload State on GPUs🌏 InternationalJournal Article
In: Sensors, vol. 22, no. 13, pp. 4899, 2022.
Abstract | Links | BibTeX | 태그: breath-first search, direction-optimizing BFS, frontier workload, GPU
@article{yoon2022surf,
title = {SURF: Direction-Optimizing Breadth-First Search Using Workload State on GPUs},
author = {Daegun Yoon and Sangyoon Oh},
url = {https://www.mdpi.com/1424-8220/22/13/4899},
doi = {https://doi.org/10.3390/s22134899},
year = {2022},
date = {2022-06-29},
urldate = {2022-01-01},
journal = {Sensors},
volume = {22},
number = {13},
pages = {4899},
publisher = {Multidisciplinary Digital Publishing Institute},
abstract = { Graph data structures have been used in a wide range of applications including scientific and social network applications. Engineers and scientists analyze graph data to discover knowledge and insights by using various graph algorithms. A breadth-first search (BFS) is one of the fundamental building blocks of complex graph algorithms and its implementation is included in graph libraries for large-scale graph processing. In this paper, we propose a novel direction selection method, SURF (Selecting directions Upon Recent workload of Frontiers) to enhance the performance of BFS on GPU. A direction optimization that selects the proper traversal direction of a BFS execution between the push and pull phases is crucial to the performance as well as for efficient handling of the varying workloads of the frontiers. However, existing works select the direction using condition statements based on predefined thresholds without considering the changing workload state. To solve this drawback, we define several metrics that describe the state of the workload and analyze their impact on the BFS performance. To show that SURF selects the appropriate direction, we implement the direction selection method with a deep neural network model that adopts those metrics as the input features. Experimental results indicate that SURF achieves a higher direction prediction accuracy and reduced execution time in comparison with existing state-of-the-art methods that support a direction-optimizing BFS. SURF yields up to a 5.62×},
keywords = {breath-first search, direction-optimizing BFS, frontier workload, GPU},
pubstate = {published},
tppubtype = {article}
}
Graph data structures have been used in a wide range of applications including scientific and social network applications. Engineers and scientists analyze graph data to discover knowledge and insights by using various graph algorithms. A breadth-first search (BFS) is one of the fundamental building blocks of complex graph algorithms and its implementation is included in graph libraries for large-scale graph processing. In this paper, we propose a novel direction selection method, SURF (Selecting directions Upon Recent workload of Frontiers) to enhance the performance of BFS on GPU. A direction optimization that selects the proper traversal direction of a BFS execution between the push and pull phases is crucial to the performance as well as for efficient handling of the varying workloads of the frontiers. However, existing works select the direction using condition statements based on predefined thresholds without considering the changing workload state. To solve this drawback, we define several metrics that describe the state of the workload and analyze their impact on the BFS performance. To show that SURF selects the appropriate direction, we implement the direction selection method with a deep neural network model that adopts those metrics as the input features. Experimental results indicate that SURF achieves a higher direction prediction accuracy and reduced execution time in comparison with existing state-of-the-art methods that support a direction-optimizing BFS. SURF yields up to a 5.62×