SHF: Small: A Hybrid NVM based Computing Architecture for Machine Learning Applications

SHF：小型：用于机器学习应用的基于混合 NVM 的计算架构

• 批准号: 1908843
• 负责人: Dong Dai
• 金额: $ 49.76万
• 依托单位: University of North Carolina at Charlotte
• 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: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908843&HistoricalAwards=false
• 关键词: SHF; Small; Hybrid; NVM; based

Today, enterprises are increasingly looking inward at their huge stores of under-processed or throwaway data, treating them as resources to be mined. As such, an emergent field called memory-intensive computing has ignited interest among industry and academia, largely driven by various emerging non-volatile memory technologies (NVMs). Machine Learning (ML) applications are being targeted by memory-intensive computing, leveraging unique properties of ML applications to improve their distributed performance by orders of magnitude. It is therefore highly desirable for ML applications executing in such memory intensive computing environments to be efficient, flexible and scalable. ML applications crunch a lot of data from disk drives, increasing latency due to disk access delays. This project explores and designs new techniques that let ML applications fully exploit the benefits of persistence for intermediate data in NVMs, which significantly reduces disk I/Os and hence data processing times. This project also involves curriculum development, and provides more avenues to bring women, minority, and underrepresented students into research and graduate programs.This project focuses on an open challenge for memory intensive computing systems how to offer ML applications with high efficiency, low cost and more flexibility, especially under the heterogeneous environment. This project proposes a hybrid NVM based computing architecture with effective data sharing and communication strategy to optimize file management, resource allocation and data communication for ML applications. This research centers on two key designs: 1) a new file and data management system based on the hybrid NVM pool consisting of Byte and Block addressable devices; 2) an efficient data sharing and communication management among memories to guarantee data consistency in the hybrid NVM memory pool.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.

如今，企业越来越多地将目光投向其庞大的未处理或一次性数据存储，将其视为待挖掘的资源。因此，一个称为内存密集型计算的新兴领域已经引起了工业界和学术界的兴趣，这主要是由各种新兴的非易失性存储器技术（NVM）驱动的。机器学习（ML）应用程序正成为内存密集型计算的目标，利用ML应用程序的独特属性将其分布式性能提高了几个数量级。因此，非常期望在这种存储器密集型计算环境中执行的ML应用是高效的、灵活的和可扩展的。ML应用程序从磁盘驱动器中处理大量数据，由于磁盘访问延迟而增加了延迟。该项目探索和设计了新技术，使ML应用程序能够充分利用NVM中中间数据持久化的好处，从而显着减少磁盘I/O，从而减少数据处理时间。该项目还涉及课程开发，并提供更多的途径，使妇女，少数民族，和代表性不足的学生进入研究和研究生课程。该项目的重点是一个开放的挑战，内存密集型计算系统如何提供ML应用程序的高效率，低成本和更灵活，特别是在异构环境下。该项目提出了一种基于混合NVM的计算架构，具有有效的数据共享和通信策略，以优化ML应用程序的文件管理，资源分配和数据通信。本课题主要研究了两个关键设计：1）基于字节和块混合存储池的文件和数据管理系统;（二）存储器之间的有效数据共享和通信管理，以保证混合NVM存储池中的数据一致性。该奖项反映了NSF的法定使命，并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

Participant Selection for Hierarchical Federated Learning in Edge Clouds

• DOI: 10.1109/nas55553.2022.9925313
• 发表时间: 2022-10
• 期刊: 2022 IEEE International Conference on Networking, Architecture and Storage (NAS)
• 作者: Xinliang Wei;Jiyao Liu;Xinghua Shi;Yu Wang

A Shared Memory Cache Layer across Multiple Executors in Apache Spark

Apache Spark 中跨多个执行器的共享内存缓存层

• DOI: 10.1109/bigdata50022.2020.9378179
• 发表时间: 2020
• 期刊: 2020 IEEE International Conference on Big Data (Big Data
• 作者: Rang, Wei;Yang, Donglin;Cheng, Dazhao
• 通讯作者: Cheng, Dazhao

Quantum Assisted Scheduling Algorithm for Federated Learning in Distributed Networks

• DOI: 10.1109/icccn58024.2023.10230094
• 发表时间: 2023-07
• 期刊: 2023 32nd International Conference on Computer Communications and Networks (ICCCN)
• 作者: Xinliang Wei;Lei Fan;Yuanxiong Guo;Yanmin Gong;Zhu Han;Yu Wang

Joint Optimization of MapReduce Scheduling and Network Policy in Hierarchical Data Centers

分层数据中心MapReduce调度与网络策略联合优化

• DOI: 10.1109/tcc.2019.2961653
• 发表时间: 2019
• 期刊: IEEE Transactions on Cloud Computing
• 影响因子: 6.5
• 作者: Yang, Donglin;Cheng, Dazhao;Rang, Wei;Wang, Yu
• 通讯作者: Wang, Yu

Elastic Executor Provisioning for Iterative Workloads on Apache Spark

Apache Spark 上迭代工作负载的弹性执行器配置

• DOI: 10.1109/bigdata47090.2019.9006021
• 发表时间: 2019
• 期刊: 2019 IEEE International Conference on Big Data (Big Data
• 作者: Yang, Donglin;Rang, Wei;Cheng, Dazhao;Wang, Yu;Tian, Jiannan;Tao, Dingwen
• 通讯作者: Tao, Dingwen