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品种越来越多。 重要的应用领域同时以数据为中心，许多最重要的设计挑战围绕着获得大型，结构丰富的信息来源的访问。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，使其运动和安置自动化，并保护其免受意外损失或腐败的影响。通过利用程序员的应用程序，施加行为，绩效，保护和可容忍目标的程序员规格（以及在可能的情况下以及开发的技术）中有效的数据，并将其推导出来，并有效地移动数据，并将其纳入不合格的技术，并将其纳入范围，并将其纳入不合格的技术，并将其纳入不合格的技术。不同的带宽，延迟和持久性能。 通过新的持续段抽象提供快速，方便地访问字节 - 可观的非易失性内存。 目标平台的范围从单独的移动或台式机到带有“分解”内存叶片的机架级系统。随着计算进入大数据时代，用于管理大型，快速，持久的记忆的技术有可能促进整个人类企业，工业，工业，科学，科学，艺术和娱乐活动的突破性。 学生将通过在罗切斯特大学的操作系统，编程语言，编程语言以及平行和分布式计算课程中的新教学模块以及在记忆技术和管理技术方面获得经验。 在实验室和教室中，在传统上代表性不足的群体之间的参与付出的努力将补充现有的部门活动，包括参与该项目下开发的ABI/HMC Braid Initiative.Software和数据，将使用GitHub等平台进行标准的开放源牌照，并在三年后可以使用诸如GitHub之类的平台获得。 它们将与Google，Facebook，IBM，Intel，AMD，Oracle等的工业联系人共享。 将在http://www.cs.rochester.edu/u/sandhya/publications.html和http://wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww.cs.rochester一下 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.

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

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.

Fast Intra-kernel Isolation and Security with IskiOS

• DOI: 10.1145/3471621.3471849
• 发表时间: 2019-03
• 期刊: Proceedings of the 24th International Symposium on Research in Attacks, Intrusions and Defenses
• 作者: Spyridoula Gravani;Mohammad Hedayati;J. Criswell;M. Scott