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.

该奖项支持针对由许多平行记忆库和磁盘组成的异质存储器和存储系统中的可扩展性问题的大型项目的计划，特别是针对大数据和深入学习应用程序。在计算机系统中，新兴的大数据和学习应用程序随着时间的推移会大大增加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的不可忽略的争论和负载不足问题。这样的问题可能会使计算可伸缩性脱轨。 为了增长对现实世界影响的必要规模，需要采用跨层方法来优化整个硬件/软件堆栈。该项目支持一个跨学科的计划过程，来自计算机架构，高性能计算（HPC），安全和隐私，系统和算法的研究人员。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。 PIS利用应用程序暗示了在完整的硬件/软件堆栈上进行其他采样灵活性，以减少内存和输入/输出之间的数据传输流量的量。这旨在实现最终目标，即在低级记忆和存储中维持共享的架构，从而实现高可扩展性。具体的研究推力是：1）为各种应用进行工作量和应用程序特征研究，例如自主驾驶和高性能计算，2）探索一种创新的方式来开发一种以数据为中心的，数据中心的任务调度程序，用于并行记忆库和磁盘，以实现高规模的应用程序，以实现高规模的应用程序，3）增强型号的应用程序，并增强了4个机器人的目标，并增加了4个机器的任务，并提高了4次启动，并提高了4个机器的任务。敏感数据集在数据分析和采样应用中的加密。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。

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

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

A Multi-Level DPM Approach for Real-Time DAG Tasks in Heterogeneous Processors

异构处理器中实时 DAG 任务的多级 DPM 方法

• DOI: 10.1109/rtss52674.2021.00014
• 发表时间: 2021
• 期刊: 2021 IEEE Real-Time Systems Symposium (RTSS
• 作者: Reghenzani, Federico;Bhuiyan, Ashikahmed;Fornaciari, William;Guo, Zhishan
• 通讯作者: Guo, Zhishan

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