FoMR: Improving Microprocessor IPC for Data Center Workloads

FoMR：改进数据中心工作负载的微处理器 IPC

• 批准号: 1823559
• 负责人: Heiner Litz
• 金额: $ 17.3万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1823559&HistoricalAwards=false
• 关键词: FoMR; Improving; Microprocessor; IPC; Data

More and more companies and institutions are moving their products and services into the cloud, increasing the number of compute cycles spent in data centers significantly. The explosive growth of such cloud computation has delivered great value to society at the cost of increasing the environmental footprint on our world. This project can improve the energy efficiency of data center systems through the design and optimization of cloud application-specific microprocessors. The research will be infused into the undergraduate curriculum at University of California Santa Cruz, educating the next generation of students on designing energy efficient, data center specific architecture and systems.This project will develop machine learning techniques to predict datacenter application behavior and apply them to processor microarchitecture. In particular, a new cross-layer approach will be developed to improve the instructions-per-cycle (IPC) performance of data center workload-optimized processors. It will explore and develop solutions for a number of challenges including offline learning of data center application behavior, leveraging machine learning models for instruction prefetching and branch prediction, and building resource and power efficient hardware to leverage the machine learning models at execution time.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.

越来越多的公司和机构正在将其产品和服务转移到云端，从而显着增加数据中心的计算周期数量。这种云计算的爆炸性增长为社会带来了巨大的价值，但代价是增加了我们世界的环境足迹。该项目可以通过云应用专用微处理器的设计和优化来提高数据中心系统的能源效率。该研究将融入加州大学圣克鲁斯分校的本科课程中，教育下一代学生设计节能的数据中心特定架构和系统。该项目将开发机器学习技术来预测数据中心应用行为并将其应用于处理器微架构。特别是，将开发一种新的跨层方法来提高数据中心工作负载优化处理器的每周期指令（IPC）性能。它将探索和开发针对许多挑战的解决方案，包括数据中心应用程序行为的离线学习、利用机器学习模型进行指令预取和分支预测，以及构建资源高效和节能的硬件以在执行时利用机器学习模型。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

Classifying Memory Access Patterns for Prefetching

• DOI: 10.1145/3373376.3378498
• 发表时间: 2020-03
• 期刊: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems
• 作者: Grant Ayers;Heiner Litz;Christos Kozyrakis;Parthasarathy Ranganathan

SSP: Eliminating Redundant Writes in Failure-Atomic NVRAMs via Shadow Sub-Paging

• DOI: 10.1145/3352460.3358326
• 发表时间: 2019-10
• 期刊: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture
• 作者: Yuanjiang Ni;Jishen Zhao;Heiner Litz;Daniel Bittman;E. Miller

Ripple: Profile-Guided Instruction Cache Replacement for Data Center Applications

• DOI: 10.1109/isca52012.2021.00063
• 发表时间: 2021-06
• 期刊: 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA)
• 作者: Tanvir Ahmed Khan;Dexin Zhang;Akshitha Sriraman;Joseph Devietti;Gilles A. Pokam;Heiner Litz;Baris Kasikci

Twig: Profile-Guided BTB Prefetching for Data Center Applications

Twig：数据中心应用程序的配置文件引导 BTB 预取

• DOI: 10.1145/3466752.3480124
• 发表时间: 2021
• 期刊: 54th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO ’21
• 作者: Khan, Tanvir Ahmed;Brown, Nathan;Sriraman, Akshitha;Soundararajan, Niranjan K;Kumar, Rakesh;Devietti, Joseph;Subramoney, Sreenivas;Pokam, Gilles A;Litz, Heiner;Kasikci, Baris
• 通讯作者: Kasikci, Baris

PDede: Partitioned, Deduplicated, Delta Branch Target Buffer

• DOI: 10.1145/3466752.3480046
• 发表时间: 2021-10
• 期刊: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture
• 作者: N. Soundararajan;Peter Braun;Tanvir Ahmed Khan;Baris Kasikci;Heiner Litz;S. Subramoney