Fast Iterative Graph Computing with Updated Neighbor States

Guardat en:
Dades bibliogràfiques
Publicat a:arXiv.org (Jul 16, 2024), p. n/a
Autor principal: Zhou, Yijie
Altres autors: Gong, Shufeng, Yao, Feng, Chen, Hanzhang, Song, Yu, Liu, Pengxi, Zhang, Yanfeng, Yu, Ge, Jeffrey Xu Yu
Publicat:
Cornell University Library, arXiv.org
Matèries:
Accés en línia:Citation/Abstract
Full text outside of ProQuest
Etiquetes: Afegir etiqueta
Sense etiquetes, Sigues el primer a etiquetar aquest registre!

MARC

LEADER 00000nab a2200000uu 4500
001 3083766549
003 UK-CbPIL
022 |a 2331-8422 
035 |a 3083766549 
045 0 |b d20240716 
100 1 |a Zhou, Yijie 
245 1 |a Fast Iterative Graph Computing with Updated Neighbor States 
260 |b Cornell University Library, arXiv.org  |c Jul 16, 2024 
513 |a Working Paper 
520 3 |a Enhancing the efficiency of iterative computation on graphs has garnered considerable attention in both industry and academia. Nonetheless, the majority of efforts focus on expediting iterative computation by minimizing the running time per iteration step, ignoring the optimization of the number of iteration rounds, which is a crucial aspect of iterative computation. We experimentally verified the correlation between the vertex processing order and the number of iterative rounds, thus making it possible to reduce the number of execution rounds for iterative computation. In this paper, we propose a graph reordering method, GoGraph, which can construct a well-formed vertex processing order effectively reducing the number of iteration rounds and, consequently, accelerating iterative computation. Before delving into GoGraph, a metric function is introduced to quantify the efficiency of vertex processing order in accelerating iterative computation. This metric reflects the quality of the processing order by counting the number of edges whose source precedes the destination. GoGraph employs a divide-and-conquer mindset to establish the vertex processing order by maximizing the value of the metric function. Our experimental results show that GoGraph outperforms current state-of-the-art reordering algorithms by 1.83x on average (up to 3.34x) in runtime. 
653 |a Algorithms 
653 |a Iterative methods 
653 |a Graph theory 
653 |a Computing time 
700 1 |a Gong, Shufeng 
700 1 |a Yao, Feng 
700 1 |a Chen, Hanzhang 
700 1 |a Song, Yu 
700 1 |a Liu, Pengxi 
700 1 |a Zhang, Yanfeng 
700 1 |a Yu, Ge 
700 1 |a Jeffrey Xu Yu 
773 0 |t arXiv.org  |g (Jul 16, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3083766549/abstract/embedded/BH75TPHOCCPB476R?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2407.14544