Collaborative Research: CSR: Medium: Towards A Unified Memory-centric Computing System with Cross-layer Support

协作研究：CSR：中：迈向具有跨层支持的统一的以内存为中心的计算系统

• 批准号: 2310422
• 负责人: Xian-He Sun
• 金额: $ 75万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310422&HistoricalAwards=false
• 关键词: Collaborative; Research; CSR; Medium; Towards

Data-centric applications, from scientific simulation to emerging machine learning and data mining algorithms, are becoming prevalent. Memory-centric computing is a potential solution to overcome the unprecedented memory performance bottleneck for such applications. We propose an integrated, full-stack, cross-layer system to enable Unified Memory-centric Computing (UniMCC). We target systems that have near-memory data processors (NDPs) as well as a disaggregated shared memory pool. We work on the entire system stack to utilize these newly proposed hardware solutions from a unified viewpoint of architecture, SW/HW interface, code generation and runtime support, and performance modeling and optimization. The goal of the proposed UniMCC system is to maximize the potential of NDPs and disaggregated memories and lift memory-centric computing to a new level. The outcomes of the project will have significant broader impacts on research communities, society, and education. With the cross-layer collaborative design, the proposed research can bring state-of-the-art techniques together to make the memory-centric paradigm more feasible. The success of the project will enable experiments and deployment of data-centric applications at larger scales, facilitating research in the AI area and improving the availability of large-scale data mining and machine learning systems for solving real-world problems. Besides training Ph.D. students, this project will deliver several course developments, curricular update activities, and outreach activities to broaden participation in computing.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.

从科学模拟到新兴的机器学习和数据挖掘算法，以数据为中心的应用程序变得普遍。以内存为中心的计算是克服此类应用程序前所未有的内存性能瓶颈的潜在解决方案。我们提出了一个集成的，全堆栈的跨层系统，以启用统一以内存为中心的计算（UNIMCC）。我们针对具有接近内存数据处理器（NDP）的系统以及分组的共享内存池。我们从统一的体系结构，SW/HW接口，代码生成和运行时支持以及性能建模和优化的统一观点来利用整个系统堆栈，以利用这些新提出的硬件解决方案。建议的UNIMCC系统的目的是最大程度地提高NDP和分解记忆的潜力，并将以内存为中心的计算提高到新的水平。该项目的结果将对研究社区，社会和教育产生更广泛的影响。借助跨层协作设计，拟议的研究可以将最新的技术融合在一起，以使以内存为中心的范式更可行。该项目的成功将使以较大规模的数据为中心的实验和部署以数据为中心的应用程序，从而促进AI领域的研究，并提高大规模数据挖掘和机器学习系统的可用性，以解决现实世界中的问题。除了培训博士学位该项目的学生将提供几个课程的发展，课程更新活动和外展活动，以扩大计算的参与。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来获得支持的。