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通常在时间窗口后失去其价值。因此，为了从数据中获得可行的智能，流媒体分析（即分析动作数据的能力）越来越变得至关重要。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记忆组成。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的挑战 - 从应用程序到目标平台 - 需要解决。在这方面，该计划项目正在确定算法和应用程序，系统软件，硬件软件共同设计和计算机体系结构中一系列全面的研究挑战，目标，关键创新和时间表。该项目将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，其兴趣在于构建针对各种可扩展系统的流媒体数据科学应用程序。该项目正在展示有关如何在驾驶应用程序时使用隐私保护型智能电网的流式学习的私密性来改进其将如何实现重大的跨堆栈性能改进的初步结果。现代数据科学应用的特征是高度分散，分布式，分布式分布式，并需要在数以千计的Edge Assistics和数百万种集中的分析中，需要在云中进行众多的分析，并需要在云中进行全面的分析。 数据。为了启用大挑战流媒体数据科学应用程序的可扩展性能，需要一个允许开发人员无缝构建针对各种可扩展系统的这些应用程序的框架。该计划项目正在进行初步研究，以开发开放源框架，流媒体软件的大型建议，该框架将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，他们将作为流媒体框架的早期采用者和开发人员。在与域专家协商中，正在评估其现有的最新算法和硬件IPS的关键数据科学内核列表，以确定性能限制和改进机会。该项目还阐明了新型抽象的要求，这些要求可以代表和在异质平台上的流数据上操作。该项目使用隐私图表学习以确保安全的智能网格作为一种激励应用，以使用新颖的共生可伸缩性概念来显示端到端可伸缩性的初步证据，从而捕获了流软件的跨层优化的影响。 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.

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