Collaborative Research:PPoSS:Planning: Streamware - A Scalable Framework for Accelerating Streaming Data Science

合作研究：PPoSS：规划：Streamware - 加速流数据科学的可扩展框架

• 批准号: 2119816
• 负责人: Viktor Prasanna
• 金额: $ 12.46万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119816&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Streamware

In grand-challenge scientific applications, the enormous amount of data produced by the sensing and instrumentation infrastructure often loses its value after a small window of time. Thus, to obtain actionable intelligence from the data, streaming analytics, i.e., the ability to analyze in-motion data, is increasingly becoming critical. Moreover, modern computing systems are highly heterogeneous, consisting of processors, accelerators, and large high-bandwidth external memories. To develop scalable streaming analytics applications, challenges across the full system stack -- from application to target platform -- need to be addressed. In this regard, this planning project is identifying a comprehensive set of research challenges, goals, key innovations and timelines in algorithms and applications, systems software, hardware-software co-design, and computer architecture. This project is bringing together a community of application developers and users, computer scientists, and data scientists, whose interests lie in building streaming data science applications targeting a wide variety of scalable systems. This project is demonstrating preliminary results on how it will achieve significant cross-stack performance improvements using Privacy Preserving Streaming Graph Learning for Secure Smart Grids as the driving application.Modern data-science applications are characterized as being highly decentralized, distributed and requiring composition and orchestration between localized analytics on thousands or millions of edge platforms and massive centralized analytics in cloud/data centers, as well as requiring real-time analytics on streaming data. To enable scalable performance of grand-challenge streaming data-science applications, a framework that allows developers to seamlessly build these applications targeting a wide variety of scalable systems is needed. This planning project is conducting preliminary research towards a large proposal for developing an opensource framework, StreamWare, that will enable users to develop streaming data-science applications. This project is establishing a community of application developers and users, computer scientists, and data scientists who would serve as early adopters and developers of the StreamWare framework. In consultation with domain experts, a list of key data-science kernels for StreamWare is being generated, and their existing state-of-the-art algorithms and hardware IPs are being evaluated to identify performance limitations and opportunities for improvement. This project is also articulating the requirements of novel abstractions that can represent and operate on streaming data on heterogeneous platforms. This project uses Privacy Preserving Streaming Graph Learning for Secure Smart Grids as a motivating application to show preliminary evidence of end-to-end scalability using a novel notion of symbiotic scalability that captures the impact of StreamWare's cross-layer optimizations. The expected outcomes of this planning project include a proposal for the research activities to be carried out in the large grant, publications on the results of the survey activities and future research directions for enabling streaming data science, and curricula for future graduate and undergraduate courses.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.

在具有重大挑战的科学应用中，传感和仪器基础设施产生的大量数据往往在一小段时间后就失去了价值。因此，为了从数据中获得可操作的情报，流分析，即，分析动态数据的能力变得越来越重要。此外，现代计算系统是高度异构的，由处理器、加速器和大型高带宽外部存储器组成。为了开发可扩展的流分析应用程序，需要解决整个系统堆栈（从应用程序到目标平台）的挑战。在这方面，该规划项目正在确定一套全面的研究挑战，目标，关键创新和时间表的算法和应用程序，系统软件，硬件软件协同设计和计算机体系结构。该项目汇集了应用程序开发人员和用户，计算机科学家和数据科学家，他们的兴趣在于构建针对各种可扩展系统的流数据科学应用程序。该项目展示了如何使用用于安全智能电网的隐私保护流图学习作为驱动应用程序来实现显著的跨堆栈性能改进的初步结果。现代数据科学应用的特点是高度分散、分布式，需要在数千或数百万个边缘平台上的本地化分析与云/数据中心的大规模集中式分析之间进行组合和编排，以及需要对流数据进行实时分析。为了实现具有巨大挑战性的流数据科学应用程序的可扩展性能，需要一个框架，允许开发人员无缝地构建针对各种可扩展系统的应用程序。该规划项目正在进行初步研究，以制定一个大型的开源框架StreamWare，使用户能够开发流数据科学应用程序。该项目正在建立一个由应用程序开发人员和用户、计算机科学家和数据科学家组成的社区，他们将成为StreamWare框架的早期采用者和开发者。在与领域专家协商后，正在生成StreamWare的关键数据科学内核列表，并正在评估其现有的最先进算法和硬件IP，以确定性能限制和改进机会。该项目还阐明了可以在异构平台上表示和操作流数据的新颖抽象的需求。该项目使用隐私保护流图学习安全智能电网作为激励应用程序显示端到端的可扩展性的初步证据，使用一种新的共生可扩展性的概念，捕捉StreamWare的跨层优化的影响。该规划项目的预期成果包括在大额赠款中开展的研究活动的建议，关于调查活动结果的出版物以及支持流数据科学的未来研究方向，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Input Feature Pruning for Accelerating GNN Inference on Heterogeneous Platforms

• DOI: 10.1109/hipc56025.2022.00045
• 发表时间: 2022-12
• 期刊: 2022 IEEE 29th International Conference on High Performance Computing, Data, and Analytics (HiPC)
• 作者: Jason Yik;S. Kuppannagari;Hanqing Zeng;V. Prasanna

SHARP: Software Hint-Assisted Memory Access Prediction for Graph Analytics

• DOI: 10.1109/hpec55821.2022.9926307
• 发表时间: 2022-09
• 期刊: 2022 IEEE High Performance Extreme Computing Conference (HPEC)
• 作者: Pengmiao Zhang;R. Kannan;Xiangzhi Tong;Anant V. Nori;V. Prasanna

ReSemble: reinforced ensemble framework for data prefetching

ReSemble：用于数据预取的增强型集成框架

• 发表时间: 2022
• 期刊: Storage and Analysis
• 作者: Zhang, Pengmiao;Kannan, Rajgopal;Srivastava, Ajitesh;Nori, Anant V.;Prasanna, Viktor K.
• 通讯作者: Prasanna, Viktor K.

Estimating the Impact of Communication Schemes for Distributed Graph Processing

估计通信方案对分布式图处理的影响

• 发表时间: 2022
• 期刊: 21st IEEE International Symposium on Parallel and Distributed Computing (ISPDC-2022
• 作者: Ye Tian;Kuppannagari, Sanmukh;Rose, Cesar Augusto;Wijeratne, Sasindu;Kannan, Rajgopal;Prasanna, Viktor K.
• 通讯作者: Prasanna, Viktor K.

Towards Programmable Memory Controller for Tensor Decomposition

用于张量分解的可编程内存控制器

• 发表时间: 2022
• 作者: Wijeratne, Sasindu;Wang, Ta-Yang;Kannan, Rajgopal;Prasanna Viktor
• 通讯作者: Prasanna Viktor