CNS Core:Small:A HW/SW Codesign Framework For Dynamic Composition of Disaggregated Hardware Systems Securely

CNS 核心：小型：用于安全地动态组合分解硬件系统的硬件/软件协同设计框架

• 批准号: 2225882
• 负责人: Venkatesh Akella
• 金额: $ 59.99万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225882&HistoricalAwards=false
• 关键词: CNS; Core; Small; HW; SW

Traditionally datacenters and high performance computing (HPC) facilities increase compute capacity by adding additional servers, which is increasingly becoming inefficient since emerging workloads exhibit very high peak to average memory requirements. Moreover, the fundamental building blocks of a modern data centers and HPC facilities (the processors) are becoming increasingly specialized with dedicated hardware for vector/tensor processing, deep learning, different memory pools such as DRAM, SRAM, and NVRAM, and special-purpose interconnect. Replicating such processors invariably results in hardware that is neither required nor useful all the time by all the applications. A disaggregated approach to computing system design can overcome these inefficiencies by selecting and composing the required hardware resources (e.g., accelerators, memory) to meet the requirements of a specific workflow or application. This project aims to make the design and implementation of such systems practical by addressing system security and performance. The intellectual merit of the proposed work lies in developing a new hardware software/codesign framework that is based on virtualized remote memory, object-level tracking for efficient data tiering and coherence, and flexible mechanisms to create and enforce hardware-based trusted execution environments. We will evaluate our solutions using a rigorous evaluation plan based on system-level modeling and simulation using the gem5 software infrastructure.Artificial intelligence and machine learning are expected to accelerate scientific discovery and play a very crucial role in addressing the grand challenges of the 21st century such as climate change, sustainability, and drug/vaccine discovery with broad societal impact. This project will enable training large scale machine learning models and perform large scale data analytics on disaggregated hardware systems which will allow them to be more performant and cost-effective. In addition, the modeling and simulation environment in gem5 broadly impacts computer architecture and computer systems researchers beyond this project. The models created in this project will be committed to the upstream gem5 project so that other researchers can use these models and build off of our designs. These contributions will impact the reproducibility and sustainability of this software infrastructure and advance computer architecture research in general.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.

传统上，数据中心和高性能计算（HPC）设施通过添加其他服务器来增加计算能力，因为新兴工作负载对平均内存需求表现出很高的峰值，这越来越效率。此外，现代数据中心和HPC设施（处理器）的基本构建块越来越专门使用专用硬件，用于矢量/张量处理，深度学习，不同的记忆池，例如DRAM，SRAM和NVRAM和NVRAM和SPECTION-POURPOSE INTERNECT。 复制此类处理器总是会导致所有应用程序既不需要也不有用的硬件。分解计算系统设计的方法可以通过选择和组成所需的硬件资源（例如，加速器，内存）来克服这些低效率，以满足特定工作流或应用程序的要求。该项目旨在通过解决系统的安全性和性能来实现此类系统的设计和实施。拟议工作的智力优点在于开发一个基于虚拟远程内存，对象级跟踪的新硬件软件/代码框架，用于有效的数据分层和连贯性以及灵活的机制来创建和执行基于硬件的可信执行环境。我们将使用严格的评估计划来评估我们的解决方案，该计划基于系统级建模和模拟使用GEM5软件基础架构。兵工智能和机器学习有望加速科学发现，并在解决21世纪的巨大挑战中起着至关重要的作用，例如气候变化，可持续性，可持续性，毒品/疫苗与广泛的社会影响。 该项目将使培训大规模的机器学习模型，并对分解的硬件系统进行大规模数据分析，这将使它们具有更大的性能和成本效益。此外，GEM5中的建模和仿真环境广泛影响了该项目以外的计算机架构和计算机系统研究人员。该项目中创建的模型将致力于上游GEM5项目，以便其他研究人员可以使用这些模型并建立我们的设计。这些贡献将影响该软件基础架构的可重复性和可持续性，并提高计算机架构研究。该奖项反映了NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来提供支持。