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

• 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

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的世界网络数据的强大工具。例如，图形用于表示社交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。了解图形属性并通过在极端规模下对图形进行分析来获取隐藏信息对于跨多个领域的科学进步和解决真实的世界影响力问题至关重要。云平台已被用于执行极端规模的图形分析。这导致了工作负载的指数级增长，而与此同时，云平台的性能改进速度已经放缓。为了解决这个问题，云平台正在使用加速器进行增强。然而，从这种加速器增强的云平台实现高性能所需的专业知识将限制其对更广泛的科学和工程社区的可访问性。 为了解决这个问题，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的云平台上轻松执行极端规模的图形分析工作流程。这将显著提高研究人员的生产力，因为i）研究人员将避免开发图分析算法的并行实现的陡峭学习曲线，ii）图分析的大小和规模的增加将允许研究人员以减少的延迟分析显着的大型数据集，从而丰富领域研究的质量。此外，该项目中开发的技术也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、该项目的研究成果也将成为研究生和本科生课程的教材。该项目将研究和开发高性能的图形分析算法和软件，用于跨多个科学和技术领域的关键图形工作流程和核心。工程领域。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（FPGA）和具有高速缓存一致性接口的高带宽存储器（HBM）。将开发一个集成优化框架，包括内存优化、分区和映射技术，以利用目标平台的异构性。具体而言，将开发用于优化存储器数据布局和云执行的集成优化的技术，以实现加速器增强的云平台中的可扩展性能。内存数据布局优化旨在充分利用HBM提供的高带宽，确保数据重用用于广泛的图形分析问题。拟议的软件将确保在单个异构节点架构以及具有多个异构节点的云平台上无缝并行处理整个图形。集成的优化框架将被开发成一个可扩展、可部署、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异构平台开发。通过加速云平台上的图形分析工作流程，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程，这是许多科学和工程领域的关键组成部分。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。

项目成果

Accelerating Allreduce With In-Network Reduction on Intel PIUMA

通过英特尔 PIUMA 上的网络内缩减加速 Allreduce

• DOI: 10.1109/mm.2021.3139092
• 发表时间: 2022
• 期刊: IEEE Micro
• 影响因子: 3.6
• 作者: Lakhotia, Kartik;Petrini, Fabrizio;Kannan, Rajgopal;Prasanna, Viktor
• 通讯作者: Prasanna, Viktor

Performance of Local Push Algorithms for Personalized PageRank on Multi-core Platforms

多核平台上个性化 PageRank 本地推送算法的性能

• DOI: 10.1109/hipc53243.2021.00051
• 发表时间: 2021
• 期刊: and Analytics. (HiPC
• 作者: Aggarwal, Madhav;Zhang, Bingyi;Prasanna, Viktor
• 通讯作者: Prasanna, Viktor

A High Throughput Parallel Hash Table Accelerator on HBM-enabled FPGAs

支持 HBM 的 FPGA 上的高吞吐量并行哈希表加速器

• 发表时间: 2020
• 期刊: International Conference on Field Programmable Technology (FPT
• 作者: Yang, Yang;Kuppannagari, Sanmukh R.;Prasanna, Viktor K.
• 通讯作者: Prasanna, Viktor K.

Programmable FPGA-based Memory Controller

• DOI: 10.1109/hoti52880.2021.00020
• 发表时间: 2021-08
• 期刊: 2021 IEEE Symposium on High-Performance Interconnects (HOTI)
• 作者: Sasindu Wijeratne;S. Pattnaik;Zhiyu Chen;R. Kannan;V. Prasanna

QTAccel: Generic FPGA Design for Q-Table based Reinforcement Learning Accelerators

QTAccel：基于 Q-Table 的强化学习加速器的通用 FPGA 设计

• 发表时间: 2020
• 期刊: 27th Reconfigurable Architectures Workshop (RAW
• 作者: Meng, Yuan;Kuppannagari, Sanmukh R.;Rajat, Rachit;Srivastava, Ajitesh;Kannan, Rajgopal;Prasanna, Viktor K.
• 通讯作者: Prasanna, Viktor K.