CNS Core: Medium: Reimagining the Storage Stack for Emerging Memory Technologies

CNS 核心：中：重新构想新兴内存技术的存储堆栈

• 批准号: 1900803
• 负责人: Sandhya Dwarkadas
• 金额: $ 110.26万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1900803&HistoricalAwards=false
• 关键词: CNS; Core; Medium; Reimagining; Storage

Technological innovations are leading to increasing numbers and varieties of data storage devices. Important application domains are simultaneously becoming more data centric, and many of the most significant design challenges revolve around access to large, richly structured sources of information. To help programmers manage the complexity of data and devices, this project is developing new ways to organize and access data, to automate its movement and placement, and to protect it from accidental loss or corruption.By leveraging programmer specifications of application behavior and of performance, protection, and fault tolerance objectives (and by inferring these where possible), developed techniques will efficiently and automatically move data among devices including 3D-stacked and new nonvolatile alternatives with differing bandwidth, latency, and persistence properties. Fast, convenient access to byte-addressable nonvolatile memory will be provided through a new persistent segment abstraction. Targeted platforms will range from individual mobile or desktop machines to rack-level systems with "disaggregated" memory blades.As computing moves into the era of big data, techniques to manage large, fast, persistent memories have the potential to facilitate breakthroughs across the whole range of human endeavor, in government, industry, science, the arts, and entertainment. Students will gain experience with memory technologies and management techniques through new instructional modules in operating systems, programming languages, and parallel and distributed computing courses at the University of Rochester. Efforts to increase participation among traditionally underrepresented groups, in both the laboratory and the classroom, will complement existing department activities, including participation in the ABI/HMC BRAID initiative.Software and data developed under the project will be made publicly available under standard open-source licenses using platforms such as GitHub, and will be accessible for at least three years past the end of the grant. They will be shared with industrial contacts at Google, Facebook, IBM, Intel, AMD, Oracle, and others. Publications associated with the project will be made available at http://www.cs.rochester.edu/u/sandhya/publications.html and at http://www.cs.rochester.edu/u/scott/papers.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.

技术创新导致数据存储设备的数量和种类不断增加。 重要的应用领域同时变得更加以数据为中心，许多最重要的设计挑战都围绕着访问大型、结构丰富的信息源。为了帮助程序员管理数据和设备的复杂性，该项目正在开发新的方法来组织和访问数据，自动化其移动和放置，并保护数据免受意外丢失或损坏。通过利用程序员对应用程序行为和性能、保护和容错目标的规范（并在可能的情况下推断这些目标），开发的技术将在设备之间高效、自动地移动数据，包括 3D 堆栈和新的非易失性替代方案 不同的带宽、延迟和持久性属性。 通过新的持久段抽象，将提供对字节可寻址非易失性存储器的快速、方便的访问。 目标平台范围从个人移动或台式机到具有“分解”内存刀片的机架级系统。随着计算进入大数据时代，管理大型、快速、持久内存的技术有可能促进人类在政府、工业、科学、艺术和娱乐等各个领域的突破。 学生将通过罗切斯特大学操作系统、编程语言以及并行和分布式计算课程的新教学模块获得内存技术和管理技术的经验。 增加实验室和课堂上传统上代表性不足群体的参与的努力将补充现有部门的活动，包括参与 ABI/HMC BRAID 计划。该项目开发的软件和数据将使用 GitHub 等平台在标准开源许可下公开提供，并且在资助结束后至少三年内可供访问。 它们将与谷歌、Facebook、IBM、英特尔、AMD、甲骨文等公司的行业联系人共享。 与该项目相关的出版物将在 http://www.cs.rochester.edu/u/sandhya/publications.html 和 http://www.cs.rochester.edu/u/scott/papers 上提供。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进行评估，被认为值得支持 标准。

项目成果

Understanding and optimizing persistent memory allocation

• DOI: 10.1145/3381898.3397212
• 发表时间: 2020-03
• 期刊: Proceedings of the 2020 ACM SIGPLAN International Symposium on Memory Management
• 作者: Wentao Cai;Haosen Wen;H. A. Beadle;Chris Kjellqvist;Mohammad Hedayati;M. Scott

How Should We Think about Persistent Data Structures?

我们应该如何看待持久数据结构？

• DOI: 10.1145/3519270.3538455
• 发表时间: 2022
• 期刊: 41st ACM Symposium on Principles of Distributed Computing (PODC
• 作者: Scott, Michael L.

Fast Nonblocking Persistence for Concurrent Data Structures (extended abstract)

并发数据结构的快速非阻塞持久化（扩展摘要）

• 发表时间: 2022
• 期刊: 13th Annual Non-Volatile Memories Workshop
• 作者: Cai, Wentao;Wen, Haosen;Maksimovski, Vladimir;Du, Mingzhe;Sanna, Rafaello;Abdallah, Shreif;Scott, Michael L.
• 通讯作者: Scott, Michael L.

Performance Characterization of HTAP Workloads

• DOI: 10.1109/icde51399.2021.00162
• 发表时间: 2021-04
• 期刊: 2021 IEEE 37th International Conference on Data Engineering (ICDE)
• 作者: Utku Sirin;S. Dwarkadas;A. Ailamaki

Blast from the Past: Least Expected Use (LEU) Cache Replacement with Statistical History

• DOI: 10.1145/3591195.3595267
• 发表时间: 2023-06
• 期刊: Proceedings of the 2023 ACM SIGPLAN International Symposium on Memory Management
• 作者: Sayak Chakraborti;Zhizhou Zhang;Noah Bertram;C. Ding;S. Dwarkadas