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OAC Core: Small: Scalable Graph Analytics on Emerging Cloud Infrastructure

OAC 核心：小型：新兴云基础设施上的可扩展图形分析

基本信息

批准号：
1911229
负责人：
Viktor Prasanna
金额：
$ 48.18万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-01 至 2022-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911229&HistoricalAwards=false
关键词：
OAC Core Small Scalable Graph

项目摘要

Graphs are powerful tools for representing real world networked data in a wide range of scientific and engineering domains. As examples, graphs are used to represent people and their interactions in social networks, or proteins and their functionality in biological networks, landmarks and roads in transportation networks, etc. Understanding graph properties and deriving hidden information by performing analytics on graphs at extreme scale is critical for the progress of science across multiple domains and solving real world impactful problems. Cloud platforms have been adopted to perform extreme scale graph analytics. This has led to exponential increase in the workloads while at the same time the rate of performance improvements of cloud platforms has slowed down. To address this, cloud platforms are being augmented with accelerators. However, the expertise required to realize high performance from such accelerator enhanced cloud platforms will limit their accessibility to the broader scientific and engineering community. To address this issue, this project will research and develop a toolkit to provide Graph Analytics as a Service to enable researchers to easily perform extreme scale graph analytics workflows on accelerator enhanced cloud platforms. This will significantly increase the productivity of the researchers as i) the researchers will avoid the steep learning curve of developing parallel implementation of graph analytics algorithms, and ii) the increased size and scale of graph analytics will allow researchers to analyze significantly large datasets at reduced latency thereby enriching the quality of the domain research. Moreover, the techniques developed in this project will also be applicable for performing streaming graph analytics at the "edge" for applications such as autonomous vehicles, smart infrastructure, etc. The toolkit is expected to be used in many engineering and science disciplines including power systems engineering, network biology, preventive healthcare, smart infrastructure, etc. The research conducted in this project will also constitute materials appropriate for inclusion in graduate and undergraduate courses.The project will research and develop high performance graph analytics algorithms and software for key graph workflows and kernels spanning multiple scientific and engineering domains. The target platform will be accelerator enhanced cloud platforms consisting of emerging node architectures comprising of multi-core processors, Field Programmable Gate Arrays (FPGAs) and high bandwidth memory (HBM) with cache coherent interface. An integrated optimization framework consisting of memory optimizations and partitioning and mapping techniques will be developed to exploit the heterogeneity of the target platforms. Specifically, techniques for optimal memory data layout and integrated optimizations for cloud execution will be developed to realize scalable performance in accelerator enhanced cloud platforms. The memory data layout optimization seeks to fully exploit the high bandwidth provided by HBM by ensuring data reuse for a broad class of graph analytics problems. The proposed software will ensure seamless parallel processing of the entire graph on a single heterogeneous node architecture as well as cloud platforms with multiple heterogeneous nodes. The integrated optimization framework will be developed into a scalable, deployable, robust Cyber Infrastructure (CI) toolkit to provide Graph Analytics as a Service (GAaaS). The framework will be developed using state-of-the-art heterogeneous platforms. By accelerating graph analytics workflows on cloud platforms, this project will enable researchers to perform extremely large-scale graph analytics workflows which are key components of many scientific and engineering domains.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.

图形是在广泛的科学和工程领域中表示真实世界网络数据的强大工具。例如，图被用来表示社会网络中的人及其交互，或生物网络中的蛋白质及其功能，交通网络中的地标和道路等。通过在极大尺度上对图进行分析来理解图的性质并获取隐藏的信息，对于跨多个领域的科学进步和解决现实世界的影响问题至关重要。云平台已经被用来执行极值尺度图分析。这导致工作负载呈指数级增长，同时云平台的性能提升速度有所放缓。为了解决这个问题，云平台正在增加加速器。然而，从这种加速器增强型云平台实现高性能所需的专业知识将限制更广泛的科学和工程界访问它们。为了解决这一问题，该项目将研究和开发一个工具包，以提供图形分析即服务，使研究人员能够在加速器增强型云平台上轻松执行极大规模的图形分析工作流。这将显著提高研究人员的生产率，因为i)研究人员将避免开发图形分析算法的并行实现的陡峭学习曲线，以及ii)图形分析的规模和规模的增加将允许研究人员以更短的延迟分析显著的大型数据集，从而丰富领域研究的质量。此外，该项目开发的技术还将适用于在自动驾驶汽车、智能基础设施等应用的边缘执行流图分析。该工具包预计将用于许多工程和科学学科，包括电力系统工程、网络生物学、预防性医疗保健、智能基础设施等。该项目进行的研究还将构成适合纳入研究生和本科课程的材料。该项目将研究和开发高性能图形分析算法和软件，用于跨越多个科学和工程领域的关键图形工作流程和核心。目标平台将是加速器增强型云平台，由新兴节点架构组成，包括多核处理器、现场可编程门阵列(FP GA)和具有高速缓存一致性接口的高带宽存储器(HBM)。将开发一个由内存优化以及分区和映射技术组成的集成优化框架，以利用目标平台的异构性。具体地说，将开发优化内存数据布局和云执行集成优化的技术，以实现加速器增强型云平台的可扩展性能。内存数据布局优化旨在通过确保数据重用来充分利用HBM提供的高带宽，以解决广泛类别的图形分析问题。建议的软件将确保在单个异质节点体系结构以及具有多个异质节点的云平台上无缝并行处理整个图。集成优化框架将开发成可扩展、可部署、功能强大的网络基础设施(CI)工具包，以提供图形分析即服务(GAaaS)。该框架将使用最先进的不同平台进行开发。通过在云平台上加速图形分析工作流，该项目将使研究人员能够执行超大规模的图形分析工作流，这些工作流是许多科学和工程领域的关键组件。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。