PPoSS: Planning: Data Centric Computing for Scalable Heterogeneous Memory and Storage Systems Architecture

PPoSS：规划：可扩展异构内存和存储系统架构的以数据为中心的计算

• 批准号: 2028481
• 负责人: Jun Wang
• 金额: $ 25万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028481&HistoricalAwards=false
• 关键词: PPoSS; Planning; Data; Centric; Computing

This award supports planning for a large-grant project addressing the scalability issue among heterogeneous memory and storage systems that are composed of many parallel memory banks and disks, specifically for big-data and deep-learning applications. Emerging big-data and learning applications significantly increase both frequency and volume of data transfers over time in computer systems, which consequently result in non-negligible contention and load-imbalance issues among large-scale parallel memory storage systems. Such concerns can derail computing scalability. To grow to the requisite scale for real-world impact, there is a need for a cross-layer approach that optimizes the entire hardware/software stack. This project supports an interdisciplinary planning process, with researchers from computer architecture, high-performance computing (HPC), security and privacy, systems, and algorithms. This project will also contribute to society through engaging under-represented groups from a Hispanic Serving Institution and research dissemination for computer science and engineering education and training.The grant will plan to build a research team with the capability to address the interdisciplinary scalability challenges to large-scale heterogeneous memory and storage systems. The PIs exploit application hints such as additional sampling flexibility at the full hardware/software stack to reduce the amount of data-transfer traffic among memory and Input/Output. This is intended to realize the ultimate objective, namely maintaining a shared-nothing architecture in low-level memory and storage and thus achieving high scalability. Specific research thrusts are: 1) conducting workload and application-characteristics studies for various applications such as autonomous driving and high-performance computing, 2) exploring an innovative way to develop an application-aware, data-centric task scheduler for parallel memory banks and disks to achieve high-scalability, 3) developing several machine-learning algorithms to enhance task scheduling, and 4) investigating the applications of homomorphic encryption for sensitive datasets in data analytics and sampling applications.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.

该奖项支持规划一个大型资助项目，解决由许多并行内存库和磁盘组成的异构内存和存储系统之间的可扩展性问题，特别是针对大数据和深度学习应用程序。新兴的大数据和学习应用程序显着增加了计算机系统中数据传输的频率和数量，从而导致大规模并行存储器存储系统中不可忽视的争用和负载不平衡问题。这种担忧可能会破坏计算的可伸缩性。 为了增长到现实世界影响所需的规模，需要一种跨层方法来优化整个硬件/软件堆栈。该项目支持跨学科的规划过程，研究人员来自计算机体系结构，高性能计算（HPC），安全和隐私，系统和算法。该项目还将通过吸引来自西班牙裔服务机构的代表性不足的群体以及计算机科学和工程教育和培训的研究传播来为社会做出贡献。该赠款将计划建立一个有能力解决大规模异构内存和存储系统跨学科可扩展性挑战的研究团队。PI利用应用程序提示，例如完整硬件/软件堆栈的额外采样灵活性，以减少内存和输入/输出之间的数据传输流量。这是为了实现最终目标，即在低级内存和存储中保持无共享架构，从而实现高可扩展性。具体的研究重点是：1）为各种应用（如自动驾驶和高性能计算）进行工作负载和应用特性研究，2）探索一种创新方法，为并行存储体和磁盘开发一种应用感知的、以数据为中心的任务调度器，以实现高可扩展性，3）开发几种机器学习算法，以增强任务调度，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

SSDA: Secure Source-Free Domain Adaptation

• DOI: 10.1109/iccv51070.2023.01757
• 发表时间: 2023-10
• 期刊: 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
• 作者: Sabbir Ahmed;Abdullah Al Arafat;M. N. Rizve;Rahim Hossain;Zhishan Guo;A. Rakin

Precise Mixed-Criticality Scheduling on Varying-Speed Multiprocessors

• DOI: 10.1109/tc.2022.3197078
• 发表时间: 2023-01
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Sudharsan Vaidhun;Tianning She;Qijun Gu;Sajal K. Das;Kecheng Yang;Zhishan Guo

GLARE: A Dataset for Traffic Sign Detection in Sun Glare

• DOI: 10.1109/tits.2023.3294411
• 发表时间: 2022-09
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Nicholas Gray;M. Moraes;Jiang Bian;Allen Tian;A. Wang;Haoyi Xiong;Zhishan Guo

Compositional Mixed-Criticality Systems with Multiple Executions and Resource-Budgets Model

• DOI: 10.1109/rtas58335.2023.00013
• 发表时间: 2023-05
• 期刊: 2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS)
• 作者: Abdullah Al Arafat;Sudharsan Vaidhun;Liangkai Liu;Kecheng Yang;Zhishan Guo

Sampling Sparse Representations with Randomized Measurement Langevin Dynamics

• DOI: 10.1145/3427585
• 发表时间: 2021-02
• 期刊: ACM Transactions on Knowledge Discovery from Data (TKDD)
• 作者: Kafeng Wang;Haoyi Xiong;Jiang Bian;Zhanxing Zhu;Qian Gao;Zhishan Guo;Chengzhong Xu;Jun Huan;D. Dou