CAREER: Towards Efficient Accelerated Cloud Data Centers

职业：迈向高效加速云数据中心

• 批准号: 2047521
• 负责人: Daniel Wong
• 金额: $ 51.07万
• 依托单位: University of California-Riverside
• 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: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047521&HistoricalAwards=false
• 关键词: CAREER; Towards; Efficient; Accelerated; Cloud

Emerging large-scale compute-intensive applications, such as machine learning and big data analytics, have led to the wide-spread adoption of compute accelerators, such as Graphical Processing Units (GPUs), in cloud data centers. However, the existing cloud management software stack introduces many layers of abstraction that strip away application characteristics and hardware architectural details leading to inefficient and uncoordinated cloud management policies. This can lead to slow down of application performance and under-utilization of hardware resources, which ultimately impacts the data center's total cost of ownership. This project will design efficient accelerated cloud data centers that are performance-efficient, resource-efficient, and cost-efficient. The planned research has three main goals: (1) develop software frameworks to measure and identify the causes of inefficiencies in accelerated cloud data centers, (2) design inter-accelerator communication-aware cloud management policies, and (3) design accelerator-assisted interconnect topologies.The success of this project can improve application performance, improve hardware resource utilization, and reduce energy consumption; leading to greener data centers and reduced carbon footprint. In addition, this project will enable data centers to cost-efficiently scale computational power to keep pace with the growing societal demands of emerging machine learning and artificial intelligence applications. The tools and frameworks developed in this project will be made publicly available to facilitate the efficient integration of compute accelerators in cloud data centers. These new tools and frameworks will form the foundation for new course development, undergraduate research opportunities, and outreach efforts to train and prepare a new generation of engineers who will utilize compute accelerators and cloud resources as a first-class design choice.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.

新兴的大规模计算密集型应用程序（例如机器学习和大数据分析）导致了云数据中心中广泛采用计算加速器（例如图形处理单元（GPU））的广泛采用。但是，现有的云管理软件堆栈引入了许多抽象层，这些抽象层剥离了应用程序特征和硬件体系结构细节，从而导致效率低下且不协调的云管理策略。 这可能会导致应用程序性能放缓和硬件资源利用不足，这最终会影响数据中心的总拥有成本。该项目将设计有效的加速云数据中心，其性能高效，资源效率和成本效益。计划研究具有三个主要目标：（1）开发软件框架，以衡量和确定加速云数据中心效率低下的原因，（2）设计Inter-Accelerator通信 - 感知云管理策略，（3）设计加速器的设计加速器相互联系的拓扑。导致绿色数据中心并减少碳足迹。此外，该项目将使数据中心能够高效地扩展计算能力，从而跟上新兴机器学习和人工智能应用程序不断增长的社会需求。 该项目中开发的工具和框架将公开使用，以促进云数据中心中计算加速器的有效集成。这些新的工具和框架将构成新课程开发，本科研究机会以及宣传训练和准备新一代工程师的努力，这些工程师将利用计算加速器和云资源作为一流的设计选择。这项奖项反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的criteria crietia criteria criperia criperia rection the Issution the Alteria the Alteria奖项。

项目成果

MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers

• DOI: 10.1145/3458817.3480853
• 发表时间: 2021-10
• 期刊: SC21: International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: K. Ranganath;Joshua D. Suetterlein;J. Manzano;S. Song;Daniel Wong