.png){kind=icon}
返回
Communication-free massively distributed graph generation
.png){kind=icon}
OA
AI
DOI：10.1016/j.jpdc.2019.03.011.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Analyzing massive complex networks yields promising insights about our everyday lives. Building scalable algorithms to do so is a challenging task that requires a careful analysis and an extensive evaluation. However, engineering such algorithms is often hindered by the scarcity of publicly available datasets. Network generators serve as a tool to alleviate this problem by providing synthetic instances with controllable parameters. However, many network generators fail to provide instances on a massive scale due to their sequential nature or resource constraints. Additionally, truly scalable network generators are few and often limited in their realism. In this work, we present novel generators for a variety of network models that are frequently used as benchmarks. By making use of pseudorandomization and divide-and-conquer schemes, our generators follow a communication-free paradigm. The resulting generators are thus embarrassingly parallel and have a near optimal scaling behavior. This allows us to generate instances of up to 243 vertices and 247 edges in less than 22 min on 32 768 cores. Therefore, our generators allow new graph families to be used on an unprecedented scale. (C) 2019 Elsevier Inc. All rights reserved.
Keyword:
Graph generation
Communication-free
Distributed algorithms
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
3.8K
被引数:
4.8K
机构
引用论文
Refractive-index distributions generating as light rays a given family of curves lying on a surface
.png){kind=icon}
PREAI
分享收藏分享收藏分享收藏
查看更多内容