CNS Core:Small: Prescriptive Software Caching Using Leases

CNS Core:Small：使用租用的规范性软件缓存

• 批准号: 1909099
• 负责人: Chen Ding
• 金额: $ 49.98万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909099&HistoricalAwards=false
• 关键词: CNS; Core; Small; Prescriptive; Software

The cost and performance of a modern system depend on its memory hierarchy. Manual management of the memory hierarchy is complex and not portable. Automatic management is sub-optimal - it reacts to program behavior but does not directly utilize program knowledge. This research seeks a middle ground with a new type of cache called Lease Cache. It enables prescriptive caching by utilizing program knowledge, variable cache sizes, and multi-policy caching.Prescriptive caching takes a principled approach building on theory and optimization. It connects programming and caching directly with programming abstractions and program analysis. The research solves four problems. (1) Lease cache theory, ensuring performance no worse than Least Recently Used (LRU) cache when there is no program knowledge and optimal when there is program knowledge. (2) Lease cache optimization, including statistical caching as well as optimization for multi-policy and multi-granularity caching. (3) Locality analysis, combining static analysis and run-time sampling to analyze program locality. (4) Lease cache system, with efficient lease management including the use of approximation to reduce the overhead.Caching techniques are widely used in today's computing infrastructure from virtual memory management to server cache and memory cache. Lease cache is a new paradigm where an application can directly control cache management. Under this paradigm, future efforts by the research community and industry may substantially improve the utilization of computer memory for practically all applications and systems. Beyond its technical content, the project will develop material to teach locality theory and optimization and strive to increase the diversity and participation in computing research and development.The project will produce software and data from experiments for research papers. Source code, experimental data and metadata will be maintained in version-controlled repositories using open-source software (git and/or Mercurial). Source code will be made available through the Internet on the University website and on publicly-accessible source code repositories such as Github and/or Bitbucket. The repositories will be maintained as long as the research is on-going and remains relevant and at least for three years after the end of the project in 2022. The project page is https://roclocality.org/2019/04/12/nsf1909099/.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.

现代系统的成本和性能取决于其内存层次结构。 手动管理内存层次结构是复杂的，并且不可移植。自动管理是次优的-它对程序行为做出反应，但不直接利用程序知识。 这项研究寻求一个中间地带，一种新型的缓存称为租赁缓存。 它通过利用程序知识、可变缓存大小和多策略缓存来实现规定性缓存。规定性缓存采用基于理论和优化的原则性方法。 它将编程和缓存直接与编程抽象和程序分析联系起来。 本研究解决了四个问题。 (1)租用缓存理论，保证在没有程序知识时性能不低于最近最少使用（LRU）缓存，在有程序知识时性能最优。 (2)租赁缓存优化，包括统计缓存以及多策略和多粒度缓存优化。(3)局部性分析，结合静态分析和运行时采样来分析程序的局部性。(4)租用缓存系统，具有高效的租用管理，包括使用近似来减少开销。缓存技术广泛应用于当今的计算基础设施，从虚拟内存管理到服务器缓存和内存缓存。 租用缓存是一种新的范例，其中应用程序可以直接控制缓存管理。 在这种范式下，研究团体和工业界的未来努力可能会大大提高几乎所有应用程序和系统的计算机存储器的利用率。 除技术内容外，该项目还将开发教授局部性理论和优化的材料，并努力增加计算研究和开发的多样性和参与度。该项目将为研究论文提供软件和实验数据。 源代码、实验数据和元数据将使用开放源代码软件（git和/或Mercurial）保存在版本控制的储存库中。 源代码将通过互联网在大学网站和公开访问的源代码库，如Github和/或Bitbucket上提供。 只要研究仍在进行，并且仍然相关，并且在2022年项目结束后至少三年内，这些储存库将得到维护。 该项目的网页是https://roclocality.org/2019/04/12/nsf1909099/.This奖反映了NSF的法定使命，并已被认为值得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的支持。

项目成果

CLAM: Compiler Lease of Cache Memory

• DOI: 10.1145/3422575.3422800
• 发表时间: 2020-09
• 期刊: Proceedings of the International Symposium on Memory Systems
• 作者: Ian Prechtl;Ben Reber;C. Ding;D. Patru;Dong Chen

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

AWLCO: All-Window Length Co-Occurrence

• DOI: 10.4230/lipics.cpm.2021.24
• 发表时间: 2020-11
• 作者: Joshua Sobel;Noah Bertram;C. Ding;F. Nargesian;D. Gildea

Cache-coherent CLAM (WIP)

缓存一致性 CLAM (WIP)

• DOI: 10.1145/3519941.3535073
• 发表时间: 2022
• 期刊: Cache-Coherent CLAM (WIP
• 作者: Ding, Chen;Reber, Benjamin;Patru, Dorin
• 通讯作者: Patru, Dorin

Uniform lease vs. LRU cache: analysis and evaluation

• DOI: 10.1145/3459898.3463908
• 发表时间: 2021-06
• 期刊: Proceedings of the 2021 ACM SIGPLAN International Symposium on Memory Management
• 作者: Dong Chen;C. Ding;Fangzhou Liu;Ben Reber;Wesley Smith;Pengcheng Li