SHF:Small: Collaborative Research: Tailoring Memory Systems for Data-Intensive HPC Applications

SHF:Small：协作研究：为数据密集型 HPC 应用定制内存系统

• 批准号: 1718297
• 负责人: Qing Liu
• 金额: $ 14.8万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718297&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Tailoring

High-performance computing is of strategic importance for computational science and engineering in the United States, and is on an accelerated path to sustaining scientific discovery at much increased flops. To advance the scientific discovery to the next level and allow new science missions to be accomplished in a timely manner, it is critical to address the memory performance holistically in high-performance computing platforms. This project provides architectural and system support and optimization for building memory systems tailored for data-intensive applications, e.g., big data analytics. This project offers research and educational opportunities for both undergraduate and graduate students, and trains a new generation of computer scientists and engineers in the area of high-performance computing. The objective of this project is to address the research challenges in building an efficient memory system by designing new techniques from several aspects. It develops a novel centralized memory refresh scheme at the cluster-level to manage memory refresh overhead, which has been increasingly performance-impacting and energy-consuming. It designs a new memory scheduling policy, taking advantages of new memory characteristics. It makes memory characteristics/peculiarities be available to the processor and operating system, so that they can make well-informed decisions to fully exploit memory performance potentials. It leverages in-memory computing to enable efficient in-situ processing. The integration of all these techniques provides a holistic solution to building an efficient memory system tailored for data-intensive applications.

高性能计算对美国的计算科学和工程具有战略重要性，并且正在加速以更高的失败率维持科学发现。为了将科学发现推进到下一个水平，并允许及时完成新的科学任务，在高性能计算平台中全面解决内存性能至关重要。该项目为构建数据密集型应用程序量身定制的存储系统提供架构和系统支持和优化，例如，大数据分析该项目为本科生和研究生提供研究和教育机会，并培养新一代高性能计算领域的计算机科学家和工程师。本项目的目标是通过从几个方面设计新技术来解决构建高效存储系统的研究挑战。它开发了一种新的集中式内存刷新机制，在集群级管理内存刷新开销，这已经越来越多的性能影响和能源消耗。利用新的内存特性，设计了一种新的内存调度策略。它使内存特性/特性可用于处理器和操作系统，以便它们可以做出明智的决定，以充分利用内存性能潜力。它利用内存计算来实现高效的原位处理。所有这些技术的集成提供了一个整体的解决方案，以建立一个高效的内存系统，为数据密集型应用程序量身定制。

项目成果

Taming I/O Variation on QoS-Less HPC Storage: What Can Applications Do?

控制无 QoS HPC 存储上的 I/O 变化：应用程序可以做什么？

• DOI: 10.1109/sc41405.2020.00015
• 发表时间: 2020
• 期刊: 2020.
• 作者: Qiao, Zhenbo;Liu, Qing;Podhorszki, Norbert;Klasky, Scott;Chen, Jieyang
• 通讯作者: Chen, Jieyang

High-Ratio Lossy Compression: Exploring the Autoencoder to Compress Scientific Data

• DOI: 10.1109/tbdata.2021.3066151
• 发表时间: 2023-02
• 期刊: IEEE Transactions on Big Data
• 影响因子: 7.2
• 作者: Tong Liu;Jinzhen Wang;Qing Liu;Shakeel Alibhai;Tao Lu;Xubin He

Identifying Latent Reduced Models to Precondition Lossy Compression

• DOI: 10.1109/ipdps.2019.00039
• 发表时间: 2019-05
• 期刊: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: Huizhang Luo;Dan Huang;Qing Liu;Zhenbo Qiao;Hong Jiang;J. Bi;Haitao Yuan;Mengchu Zhou;Jinzhen Wang;Zhenlu Qin

A Comprehensive Study of In-Memory Computing on Large HPC Systems

大型 HPC 系统内存计算的综合研究

• DOI: 10.1109/icdcs47774.2020.00045
• 发表时间: 2020
• 期刊: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS
• 作者: Huang, Dan;Qin, Zhenlu;Liu, Qing;Podhorszki, Norbert;Klasky, Scott
• 通讯作者: Klasky, Scott

Can I/O Variability Be Reduced on QoS-Less HPC Storage Systems?

• DOI: 10.1109/tc.2018.2881709
• 发表时间: 2019-05
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Dan Huang;Qing Liu;J. Choi;N. Podhorszki;S. Klasky;Jeremy S. Logan;G. Ostrouchov;Xubin He;M. Wolf