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CAREER: Scalable Graph Processing to a Quadrillion Edges and Beyond

职业：可扩展至四万亿边缘及以上的图形处理

基本信息

批准号：
2047821
负责人：
George Slota
金额：
$ 48.81万
依托单位：
Rensselaer Polytechnic Institute
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-15 至 2026-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047821&HistoricalAwards=false
关键词：
CAREER Scalable Graph Processing Quadrillion

项目摘要

Graph-structured relational data is ubiquitous throughout the social and physical sciences. Such datasets include personal relations in social and communication networks, protein-protein interactions within an organism, and particle interactions in physics simulations, among many other examples. By studying these graphs, researchers can develop techniques to identify harmful actors in social networks, discover novel protein interaction pathways, and better model interactions within a physical system. However, the large scale and complexity of these datasets makes them particularly challenging to study, and studies often require computationally-expensive analytical techniques. These challenges are further exacerbated by the complexity of the modern large-scale parallel computational systems on which such studies are often performed. Solving these challenges enables the real-time analysis of large-scale constantly-evolving social networks, in-depth studies of full-scale brain neural connectome graphs, and the general application of computationally-intensive analytics to other massive relational datasets. The research in this project presents a set of highly inter-related approaches designed to concurrently address these challenges. Educational initiatives of this project include the development of classes that will introduce students in computer science, the physical sciences, and the social sciences to various aspects of graph theory and computational graph analytics. High school through graduate students are being engaged as contributing members of the project's various research goals. These initiatives are further fostering involvement of students in research, high performance computing, and open source software development.Specifically, the research in this project is aimed at developing methods to enable complex computations on quadrillion+ edge graphs using current petascale and forthcoming exascale high performance computing platforms. These methods fall under three broad thrusts. The first thrust relates to "Graph Layout", which is the way in which a graph dataset is partitioned, ordered, and stored in-memory and out-of-core on a computational system. An outcome of this thrust is a high quality and scalable means to optimize graph layout under consideration of data type, algorithmic pattern, and hardware platform. The second thrust considers "Architecture-centric Processing" of these datasets under consideration of modern high performance systems. This thrust is researching how to efficiently map complex graph analytic problems to complex heterogeneous architectures, while considering multilevel computational models, asynchronous computations, and various graph layout methodologies. The third thrust involves the "Development of Scalable and Open-source Software" to enable the broader scientific community to easily address the challenges of the prior thrusts as it relates to their specific dataset, analytical problem, and hardware. This thrust is investigating how to best develop software frameworks and toolkits that are designed to scale to the massive (quadrillion+ edge) and irregular power-law graphs arising from these various domains, while efficiently running on next-generation exascale hardware.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

图结构的关系数据在社会科学和物理科学中无处不在。这些数据集包括社交和通信网络中的个人关系，生物体内的蛋白质-蛋白质相互作用，以及物理模拟中的粒子相互作用等。通过研究这些图，研究人员可以开发技术来识别社交网络中的有害行为者，发现新的蛋白质相互作用途径，并更好地模拟物理系统中的相互作用。然而，这些数据集的大规模和复杂性使得它们的研究特别具有挑战性，并且研究通常需要计算昂贵的分析技术。这些挑战进一步加剧了现代大规模并行计算系统的复杂性，这些研究经常在这些系统上进行。解决这些挑战可以实现对大规模不断发展的社交网络的实时分析，对全尺度大脑神经连接图的深入研究，以及将计算密集型分析应用于其他大规模关系数据集。该项目的研究提出了一套高度相关的方法，旨在同时应对这些挑战。该项目的教育举措包括开发课程，向计算机科学，物理科学和社会科学的学生介绍图论和计算图分析的各个方面。从高中到研究生都在为该项目的各种研究目标做出贡献。这些举措进一步促进学生参与研究，高性能计算和开源软件开发。具体来说，本项目的研究旨在开发方法，使用当前的千万亿次和即将到来的百万亿次高性能计算平台，在四边形+边缘图上实现复杂计算。这些方法分为三大类。第一个推力涉及“图形布局”，这是图形数据集在计算系统上被分区、排序和存储在内存中和核外的方式。这种推力的结果是一种高质量和可扩展的方法，可以在考虑数据类型，算法模式和硬件平台的情况下优化图形布局。第二个推力考虑了现代高性能系统下这些数据集的“以架构为中心的处理”。这个推力是研究如何有效地映射复杂的图分析问题，复杂的异构体系结构，同时考虑多级计算模型，异步计算，和各种图形布局方法。第三个重点是“可扩展和开源软件的开发”，以使更广泛的科学界能够轻松解决与特定数据集、分析问题和硬件相关的先前重点的挑战。该奖项旨在研究如何最好地开发软件框架和工具包，以扩展到这些不同领域产生的大规模（四边形+边）和不规则幂律图，同时有效地运行在下一代艾级硬件上。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。