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

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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.