CSR: Small: Decoupling File System from Volatile Main Memory: A First Step towards a Single-Level Persistent Store

CSR：小：将文件系统与易失性主内存解耦：迈向单级持久存储的第一步

• 批准号: 1813485
• 负责人: Tao Xie
• 金额: $ 33.69万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813485&HistoricalAwards=false
• 关键词: CSR; Small; Decoupling; File; System

A file system is a computer software module, which is in charge of how files are named, stored, and retrieved. Existing memory systems are based on dynamic random-access memory (DRAM), which is reaching its density and power ceiling. Emerging persistent memory technologies like phase change memory (PCM) not only provide a denser energy-efficient alternative to DRAM, but also allow file systems to be built atop them. This project will contribute to memory and storage technologies by developing a new single-level persistent memory architecture and a new file system dedicated to it. The new architecture will turn a small-size DRAM-based main memory system to a large-capacity persistent memory system that can access files in-place. This project will proceed along two thrusts. First, it will bridge the technology gap in building high-performance persistent memory systems with an in-depth investigation. Second, it will develop the first file system devoted to a single-level persistent store to efficiently managing data, which is increasingly demanded by data-intensive applications.This project will benefit society by developing high-performance memory systems that will significantly improve the performance and energy-efficiency of future big data applications, which are revolutionizing all aspects of human lives ranging from enterprises to consumers, from science to government. In the long term, techniques developed in this project will be transferable to servers/clusters and even to large-scale distributed storage systems for big data applications, where performance requirements are more stringent. This project will also promote teaching, learning, and training by exposing students to technological and scientific underpinnings in the field of big data storage systems. The project outcomes including papers published, technical reports, presentations, course modules, and a repository of the software code will be made available for free download at the address http://taoxie.sdsu.edu/, where they will be kept for ten years. The source code of the new persistent file system will also be posted on GitHub.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.

文件系统是计算机软件模块，它负责文件的命名，存储和检索。现有的内存系统基于动态随机存储器（DRAM），该内存已达到其密度和功率上限。新兴的持续内存技术（例如相变内存（PCM））不仅为DRAM提供了较密集的节能替代方案，而且还允许在其顶部构建文件系统。该项目将通过开发新的单级持久内存体系结构和专用于其的新文件系统来为内存和存储技术做出贡献。新的体系结构将将基于小型DRAM的主存储系统转换为可以在就位访问文件的大容量的持久存储系统。该项目将沿两个推力进行。首先，它将弥合技术差距，以深入研究建立高性能的持久记忆系统。其次，它将开发第一个专门用于单层持久商店以有效管理数据的文件系统，该数据越来越多地受到数据密集型应用程序的要求。该项目将通过开发高性能的记忆系统来使社会受益，从而显着提高未来大​​数据应用程序的绩效和能源效率，从而使人们从肠道政府范围内彻底彻底彻底改变了企业范围内的人，从而使消费者从肠道上革新企业。从长远来看，该项目中开发的技术将可以转移到服务器/群集，甚至可以转移到大数据应用程序的大规模分布式存储系统，在此情况下，在性能要求更为严格的情况下。该项目还将通过使学生了解大数据存储系统领域的技术和科学基础，从而促进教学，学习和培训。该项目的结果包括已发表的论文，技术报告，演示文稿，课程模块以及软件代码的存储库，可在地址http://taoxie.sdsu.edu/上免费下载，其中将保存十年。新的持久文件系统的源代码也将发布在GitHub上。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

A Memory-Access-Efficient Adaptive Implementation of kNN on FPGA through HLS

通过 HLS 在 FPGA 上实现 kNN 的内存访问高效自适应实现

• DOI: 10.1109/iccd46524.2019.00030
• 发表时间: 2019
• 期刊: IEEE International Conference on Computer Design
• 作者: Song, Xiaojia;Xie, Tao;Fischer, Stephen
• 通讯作者: Fischer, Stephen

BORA: A Bag Optimizer for Robotic Analysis

• DOI: 10.1109/sc41405.2020.00016
• 发表时间: 2020-11
• 期刊: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: Jian Zhang-;Tao Xie;Yuzhuo Jing;Yanjie Song;Guanzhou Hu;Si Chen;Shu Yin

A Near-Data Processing Server Architecture and Its impact on Data Center applications

近数据处理服务器架构及其对数据中心应用的影响

• DOI: 10.1007/978-3-030-20656-7_5
• 发表时间: 2019
• 期刊: The ISC High Performance
• 作者: Xiaojia Song, Tao Xie

Two Reconfigurable NDP Servers: Understanding the Impact of Near-Data Processing on Data Center Applications

两台可重新配置的 NDP 服务器：了解近数据处理对数据中心应用程序的影响

• DOI: 10.1145/3460201
• 发表时间: 2021
• 期刊: ACM Transactions on Storage
• 影响因子: 1.7
• 作者: Song, Xiaojia;Xie, Tao;Fischer, Stephen
• 通讯作者: Fischer, Stephen

Accelerating kNN search in high dimensional datasets on FPGA by reducing external memory access

• DOI: 10.1016/j.future.2022.07.009
• 发表时间: 2022-07
• 期刊: Future Gener. Comput. Syst.
• 作者: Xiaojia Song;Tao Xie;Stephen Fischer