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.

文件系统是一个计算机软件模块，负责文件的命名、存储和检索。现有的存储器系统基于动态随机存取存储器（DRAM），其密度和功率正在达到其上限。新兴的持久性存储器技术，如相变存储器（PCM），不仅提供了一个更密集的能源效率的替代DRAM，但也允许文件系统建立在他们之上。本项目将通过开发新的单级持久存储器体系结构和专用于该体系结构的新文件系统，为存储器和存储技术做出贡献。新体系结构将把基于DRAM的小尺寸主存系统转变为能够就地访问文件的大容量持久存储器系统。这个项目将沿着沿着两个方向进行。首先，它将通过深入的研究来弥合构建高性能持久存储系统的技术差距。第二，开发第一个单级持久存储文件系统，以有效管理数据密集型应用日益增长的数据需求。该项目将通过开发高性能存储系统，显著提高未来大数据应用的性能和能源效率，从而造福社会，这些应用将彻底改变从企业到消费者的人类生活的方方面面，从科学到政府从长远来看，该项目开发的技术将可转移到服务器/集群，甚至大规模分布式存储系统的大数据应用程序，其中性能要求更严格。该项目还将通过让学生接触大数据存储系统领域的技术和科学基础来促进教学，学习和培训。项目成果包括发表的论文、技术报告、演示文稿、课程模块和软件代码库，可在http：//taoxie.sdsu.edu/免费下载，并保存十年。新持久文件系统的源代码也将发布在GitHub上。该奖项反映了NSF的法定使命，并通过使用基金会的知识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

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

HART: A Concurrent Hash-Assisted Radix Tree for DRAM-PM Hybrid Memory Systems

• DOI: 10.1109/ipdps.2019.00100
• 发表时间: 2019-05
• 期刊: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: Wen Pan;Tao Xie;Xiaojia Song

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