CCF: EAGER: DeepGreen: Modeling and Boosting Accelerated Computing on Liquid Immersion Cooled HPC Systems

CCF：EAGER：DeepGreen：液浸冷却 HPC 系统的建模和加速加速计算

• 批准号: 1942182
• 负责人: Rong Ge
• 金额: $ 26.98万
• 依托单位: Clemson University
• 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 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942182&HistoricalAwards=false
• 关键词: CCF; EAGER; DeepGreen; Modeling; Boosting

Power and thermal issues have become critical design constraints that limit performance, scalability, and affordability for high performance computing systems and datacenters. It is urgent to address this challenge as servers are increasingly dense and employing accelerators. Liquid immersion cooling is a potential solution, due to its significantly better thermal conduction and lower power demand than traditional air cooling. This project explores immersion cooling to boost energy efficiency, computing capacity, and reliability of dense servers with multicore and manycore processors and accelerators. It engages both graduate and undergraduate students in research, and inspires and fosters the next generation workforce in technology & engineering. This project promotes green computing, which seeks to reduce energy cost of servers and datacenters. The project features close collaboration with a liquid immersion cooling vendor and will help technology innovation and transfer. This project targets dense servers comprising accelerators such as graphics processing units (GPUs), tensor processing units (TPUs), field programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs). It explores advanced machine learning to automatically learn from multi-physics, diverse data the impact of immersion cooling on performance, power, and thermal, and design model-assisted management schemes to meet various optimization objectives including energy efficiency, performance, and hotspot elimination. Completion of this project will advance the state-of-the-art accelerated computing and datacenter cooling in multiple aspects, including quantitative and comprehensive understanding of the benefits of immersion cooling, more accurate performance, power, and thermal modeling for accelerated systems, and intelligent management software.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.

功耗和散热问题已成为限制高性能计算系统和数据中心的性能、可扩展性和经济性的关键设计约束。随着服务器越来越密集并使用加速器，解决这一挑战迫在眉睫。液浸冷却是一种潜在的解决方案，因为它比传统空气冷却具有更好的导热性和更低的功耗。该项目探索浸入式冷却，以提高具有多核和众核处理器和加速器的密集服务器的能源效率、计算能力和可靠性。它让研究生和本科生参与研究，并激励和培养下一代技术和工程劳动力。该项目促进绿色计算，旨在降低服务器和数据中心的能源成本。该项目与液浸冷却供应商密切合作，将有助于技术创新和转移。该项目针对密集服务器，包括图形处理单元 (GPU)、张量处理单元 (TPU)、现场可编程门阵列 (FPGA) 和专用集成电路 (ASIC) 等加速器。它探索先进的机器学习，从多物理场、多样化的数据中自动学习浸入式冷却对性能、功率和热量的影响，并设计模型辅助管理方案，以满足包括能源效率、性能和热点消除在内的各种优化目标。该项目的完成将在多个方面推进最先进的加速计算和数据中心冷却，包括定量和全面地了解浸没式冷却的好处，加速系统更准确的性能、功耗和热建模，以及智能该奖项反映了 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。