Collaborative Research: PPoSS: Planning: Efficient Address Translation with Formal Guarantees for Data-Center-Scale Applications

协作研究：PPoSS：规划：有效的地址转换，为数据中心规模的应用程序提供正式保证

• 批准号: 2118830
• 负责人: Michael Bender
• 金额: $ 6.25万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118830&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Efficient

The investigators propose a bottom-up redesign of address translation, a critical bottleneck in the way computers organize where data is stored---whether the computer is a laptop or a massively parallel supercomputer. Address translation gives computer systems flexibility in placing and migrating data between RAM and disk, but it incurs an additional computational cost. The project’s novelty is to employ advanced techniques in data structures to accelerate address translation. The project’s impact will be to dramatically accelerate a component of all computational tasks, on all computers, from laptops to parallel supercomputers, and for computations ranging from weather simulations to machine learning.The investigators propose a redesign of TLBs, based on the investigators’ recent advances in stable, low-address-complexity hashing. This redesign includes improvements across the hardware/software stack, from the CPU, to hardware accelerators, to RDMA. The goal of this planning project is to establish the viability of the team’s approach in preparation for a full proposal to be submitted in the next phase. The investigators are empirically evaluating tacit assumptions behind address-translation design, providing a principled theoretical foundation for end-to-end analysis and design of naming, placement, load balancing, and translation in data-center-scale applications.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.

研究人员提出了一个自下而上的重新设计的地址转换，一个关键的瓶颈，在计算机组织的方式，数据存储-无论计算机是一台笔记本电脑或大规模并行超级计算机。 地址转换为计算机系统在RAM和磁盘之间放置和迁移数据提供了灵活性，但它会产生额外的计算成本。该项目的新奇在于采用数据结构中的先进技术来加速地址转换。 该项目的影响将是大大加速所有计算任务的一个组成部分，在所有计算机上，从笔记本电脑到并行超级计算机，以及从天气模拟到机器学习的计算。研究人员建议重新设计TLB，基于研究人员最近在稳定，低地址复杂性散列方面的进展。这种重新设计包括硬件/软件堆栈的改进，从CPU到硬件加速器，再到RDMA。 这一规划项目的目标是确定该小组的做法的可行性，为下一阶段提交的全面提案做准备。 研究人员正在对地址转换设计背后的隐性假设进行实证评估，为数据中心规模应用中的命名、放置、负载平衡和转换的端到端分析和设计提供原则性的理论基础。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标准进行评估，被认为值得支持。

项目成果

Mosaic Pages: Big TLB Reach with Small Pages

马赛克页面：小页面实现大 TLB 覆盖范围

• DOI: 10.1145/3582016.3582021
• 发表时间: 2023
• 期刊: Proc.\ 28th {ACM} International Conference on Architectural Support for Programming Languages and Operating Systems
• 作者: Gosakan, Krishnan;Han, Jaehyun;Kuszmaul, William;Mubarek, Ibrahim N.;Mukherjee, Nirjhar;Sriram, Karthik;Tagliavini, Guido;West, Evan;Bender, Michael A.;Bhattacharjee, Abhishek
• 通讯作者: Bhattacharjee, Abhishek

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

Online List Labeling: Breaking the log 2 n Barrier

在线列表标签：打破 log 2 n 障碍

• DOI: 10.1109/focs54457.2022.00096
• 发表时间: 2022
• 期刊: 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS
• 作者: Bender, Michael A.;Conway, Alex;Farach-Colton, Martin;Komlos, Hanna;Kuszmaul, William;Wein, Nicole
• 通讯作者: Wein, Nicole

When Are Cache-Oblivious Algorithms Cache Adaptive? A Case Study of Matrix Multiplication and Sorting

缓存无关算法何时具有缓存自适应性？

• 发表时间: 2022
• 期刊: ESA
• 作者: Bhattacharya, Arghya;Chowdhury, Abiyaz;Xu, Helen;Das, Rathish;Chowdhury, Rezaul Alam;Johnson, Rob;Nithyanand, Rishab;Bender, Michael A.
• 通讯作者: Bender, Michael A.

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