Collaborative Research: AF: Medium: Adventures in Flatland: Algorithms for Modern Memories

合作研究：AF：媒介：平地历险记：现代记忆算法

• 批准号: 2106699
• 负责人: Kunal Agrawal
• 金额: $ 30万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106699&HistoricalAwards=false
• 关键词: Collaborative; Research; AF; Medium; Adventures

The computer industry is in the midst of a revolution in data-storage technology that is forcing a major reevaluation of the algorithms used for moving data through the computer. Every new generation of storage hardware has required a new theoretical understanding of these algorithmic building blocks. Such theoretical improvements have had a profound impact on other fields of computer science, including databases and file systems, and even networks, operating systems, and machine learning. This project aims to develop the algorithmic solutions needed to exploit this seismic shift in storage technology.The team considers three impacts on algorithm performance that arise from new hardware technology such as nonvolatile memories and increased parallelism: (1) there are smaller gaps between levels in latency and in bandwidth, (2) many-core technologies introduce sharing effects on caches, and (3) memory hierarchies do not adhere to a standard strictly nested model. The team is investigating: (a) algorithmic problems in parallel-cache allocation and high-bandwidth-memory scheduling and allocation; (b) data structural problems that arise from different I/O cost models, including those that factor in the computational cost and/or the cost of durability; (c) extensions to the streaming and semi-streaming models, where algorithms have some amount of sequentially accessible working memory in addition to the traditional small pool of randomly accessible memory; (d) new I/O-efficient algorithms for directed graphs. The team is continuing community-building efforts to span systems and algorithms, including founding, steering, and/or running two new conferences and organizing workshops on the theory of non-volatile memory and storage.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.

计算机行业正处于数据存储技术的革命之中，这迫使人们对用于在计算机中移动数据的算法进行重大重新评估。每一代新的存储硬件都需要对这些算法构建块进行新的理论理解。这种理论上的改进对计算机科学的其他领域产生了深远的影响，包括数据库和文件系统，甚至网络，操作系统和机器学习。该项目旨在开发利用存储技术这一巨大转变所需的算法解决方案。该团队考虑了新硬件技术（如非易失性存储器和增强的并行性）对算法性能的三种影响：（1）在等待时间和带宽的级别之间存在较小的差距，（2）众核技术引入了对高速缓存的共享效应，以及（3）存储器层次结构不遵循标准的严格嵌套模型。 该小组正在调查：（a）并行高速缓存分配和高带宽存储器调度和分配中的算法问题;（B）由不同I/O成本模型引起的数据结构问题，包括计算成本和/或耐久性成本中的因素;（c）扩展流式和半流式模式，其中算法除了具有传统的小的随机可访问内存池之外，还具有一定量的顺序可访问的工作内存;（d）用于有向图的新的I/O高效算法。 该团队继续致力于社区建设，以跨越系统和算法，包括创立、指导和/或运行两个新的会议，并组织非易失性存储器和存储理论研讨会。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

Automatic HBM Management: Models and Algorithms

• DOI: 10.1145/3490148.3538570
• 发表时间: 2022-07
• 期刊: Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures
• 作者: Daniel DeLayo;Kenny Zhang;Kunal Agrawal;M. A. Bender;Jonathan W. Berry;Rathish Das;Benjamin Moseley;C. Phillips

Provably Good Randomized Strategies for Data Placement in Distributed Key-Value Stores

• DOI: 10.1145/3572848.3577501
• 发表时间: 2023-02
• 期刊: Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming
• 作者: Zhe Wang;Jinhao Zhao;Kunal Agrawal;Heyu Liu;Meng Xu;Jing Li

Online Parallel Paging with Optimal Makespan

• DOI: 10.1145/3490148.3538577
• 发表时间: 2022-07
• 期刊: Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures
• 作者: Kunal Agrawal;M. A. Bender;Rathish Das;William Kuszmaul;E. Peserico;Michele Scquizzato