Analysis of Graph Processing in Reconfigurable Devices for Edge Computing Applications

Kaan Olgu, Kris Nikov, J. Núñez-Yáñez
{"title":"Analysis of Graph Processing in Reconfigurable Devices for Edge Computing Applications","authors":"Kaan Olgu, Kris Nikov, J. Núñez-Yáñez","doi":"10.1109/DSD57027.2022.00012","DOIUrl":null,"url":null,"abstract":"Graph processing is an area that has received significant attention in recent years due to the substantial expansion in industries relying on data analytics. Alongside the vital role of finding relations in social networks, graph processing is also widely used in transportation to find optimal routes and biological networks to analyse sequences. The main bottleneck in graph processing is irregular memory accesses rather than computation intensity. Since computational intensity is not a driving factor, we propose a method to perform graph processing at the edge more efficiently. We believe current cloud computing solutions are still very costly and have latency issues. The results demonstrate the benefits of a dedicated sparse graph processing algorithm compared with dense graph processing when analysing data with low density. As graph datasets grow exponentially, traversal algorithms such as breadth-first search (BFS), fundamental to many graph processing applications and metrics, become more costly to compute. Our work focuses on reviewing other implementations of breadth-first search algorithms designed for low power systems and proposing our solution that utilises advanced enhancements to achieve a significant performance boost up to 9.2x better performance in terms of MTEPS compared to other state-of-the-art solutions with a power usage of 2.32W.","PeriodicalId":211723,"journal":{"name":"2022 25th Euromicro Conference on Digital System Design (DSD)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 25th Euromicro Conference on Digital System Design (DSD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSD57027.2022.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Graph processing is an area that has received significant attention in recent years due to the substantial expansion in industries relying on data analytics. Alongside the vital role of finding relations in social networks, graph processing is also widely used in transportation to find optimal routes and biological networks to analyse sequences. The main bottleneck in graph processing is irregular memory accesses rather than computation intensity. Since computational intensity is not a driving factor, we propose a method to perform graph processing at the edge more efficiently. We believe current cloud computing solutions are still very costly and have latency issues. The results demonstrate the benefits of a dedicated sparse graph processing algorithm compared with dense graph processing when analysing data with low density. As graph datasets grow exponentially, traversal algorithms such as breadth-first search (BFS), fundamental to many graph processing applications and metrics, become more costly to compute. Our work focuses on reviewing other implementations of breadth-first search algorithms designed for low power systems and proposing our solution that utilises advanced enhancements to achieve a significant performance boost up to 9.2x better performance in terms of MTEPS compared to other state-of-the-art solutions with a power usage of 2.32W.
边缘计算应用中可重构设备的图形处理分析
近年来,由于依赖数据分析的行业的大幅扩张,图形处理是一个受到广泛关注的领域。除了在社交网络中寻找关系的重要作用外,图处理还广泛应用于交通运输中寻找最佳路线和生物网络来分析序列。图处理的主要瓶颈是不规则的内存访问,而不是计算强度。由于计算强度不是驱动因素,我们提出了一种更有效地在边缘执行图处理的方法。我们认为当前的云计算解决方案仍然非常昂贵,并且存在延迟问题。结果表明,在分析低密度数据时,专用稀疏图处理算法比密集图处理算法更有优势。随着图数据集呈指数级增长,诸如宽度优先搜索(BFS)之类的遍历算法(许多图处理应用程序和指标的基础)的计算成本越来越高。我们的工作重点是审查为低功耗系统设计的其他宽度优先搜索算法的实现,并提出我们的解决方案,利用先进的增强功能,与功耗为2.32W的其他最先进的解决方案相比,在MTEPS方面实现了高达9.2倍的性能提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信