SHF: Small: Revamping I/O Architectures Using Machine Learning Techniques on Big Compute Machines

SHF：小型：在大型计算机上使用机器学习技术改进 I/O 架构

• 批准号: 1907765
• 负责人: Jun Wang
• 金额: $ 49.99万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907765&HistoricalAwards=false
• 关键词: SHF; Small; Revamping; Architectures; Using

In modern computer systems, the growing disparity in rapidly increasing computational speeds and slowly improving data transfer rates to/from off-chip memory and disk drives is a long-standing research challenge, often referred to as the Input/Output wall problem. This problem has become severe in today's big data and big compute era. Despite the rapid evolution in data storage technologies in recent years, the increasing heterogeneity and diversity in machines and workloads, coupled with the continued data explosion, exacerbate the speed gap between computing and disk-drive storage. There is an increasing need to develop a high-performance, and cost-effective architecture for emerging large-scale and diverse applications that is not affected by the Input/Output wall problem. The goal of this project is to leverage existing big compute resources such as graphic processing units (GPUs) and deep learning techniques to speed up the secondary storage system performance without adding new hardware. 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. This project proposes to develop new GPU-enabled online learned I/O architecture using artificial intelligence. This project entails three research thrusts: First, it will develop custom machine learning and deep learning algorithms and models for the storage system. For example, it will design a temporal-aware classification technique to attack the extreme-scale learning problem. Second, it will develop new learning solutions for core storage system modules such as prefetching, log management and garbage collection. Third, it will integrate these proposed interwoven modules into a heterogeneous GPU machine and a GPU cluster at scale. It will develop optimal parallelism solutions for internal solid-state disk (SSD) devices and Non-Volatile Memory Express (NVMe) and Peripheral Connection Interface Express (PCIe) protocol to construct an express channel, moving data directly between storage and GPUs.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.

在现代计算机系统中，快速提高的计算速度与缓慢提高的片外存储器和磁盘驱动器之间的数据传输速率之间的差距日益扩大，这是一个长期存在的研究挑战，通常称为输入/输出墙问题。在当今的大数据和大计算时代，这个问题变得更加严重。尽管近年来数据存储技术快速发展，但机器和工作负载的异构性和多样性不断增加，加上数据的持续爆炸，加剧了计算和磁盘驱动器存储之间的速度差距。人们越来越需要为新兴的大规模和多样化应用开发一种高性能、经济高效的架构，并且不受输入/输出墙问题的影响。该项目的目标是利用现有的大型计算资源，例如图形处理单元（GPU）和深度学习技术，在不添加新硬件的情况下提高辅助存储系统的性能。该项目还将通过吸引西班牙裔服务机构中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。该项目建议使用人工智能开发新的支持 GPU 的在线学习 I/O 架构。该项目包含三个研究重点：首先，它将为存储系统开发定制机器学习和深度学习算法和模型。例如，它将设计一种时间感知分类技术来解决极端规模的学习问题。其次，它将为预取、日志管理和垃圾收集等核心存储系统模块开发新的学习解决方案。第三，它将大规模地将这些相互交织的模块集成到异构 GPU 机器和 GPU 集群中。它将为内部固态磁盘 (SSD) 设备以及非易失性内存 Express (NVMe) 和外围连接接口 Express (PCIe) 协议开发最佳并行解决方案，以构建快速通道，在存储和 GPU 之间直接移动数据。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进行评估，被认为值得支持。

项目成果

Lelantus: Fine-Granularity Copy-On-Write Operations for Secure Non-Volatile Memories

Lelantus：用于安全非易失性存储器的细粒度写时复制操作

• DOI: 10.1109/isca45697.2020.00056
• 发表时间: 2020
• 期刊: International Symposium on Computer Architecture (ISCA
• 作者: Zhou, Jian;Awad, Amro;Wang, Jun
• 通讯作者: Wang, Jun

Integrating Cybersecurity Into a Big Data Ecosystem

将网络安全集成到大数据生态系统中

• DOI: 10.1109/milcom52596.2021.9652997
• 发表时间: 2021
• 期刊: MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM
• 作者: Tall, Anne M.;Zou, Cliff C.;Wang, Jun
• 通讯作者: Wang, Jun

Disperse Access Considered Energy Inefficiency in Intel Optane DC Persistent Memory Servers

• DOI: 10.1109/icdcs47774.2020.00107
• 发表时间: 2020-11
• 期刊: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)
• 作者: Daping Li;Ji-guang Wan;Jun Wang;Jian Zhou;Kai Lu;Peng Xu;Fei Wu;C. Xie