David Sickinger | Searcharxiv

arXiv2024

Integrating Energy-Efficient Computing Research to Accelerate Energy Technology

NREL's computational sciences center hosts the largest high-performance computing (HPC) capabilities dedicated to energy research while functioning as a living laboratory for energy-efficient computing. NREL's HPC capabilities support the research needs of the Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). In ten years of operation, HPC use in EERE-sponsored research has grown by a factor of 30, including work in electricity generation, energy efficiency, transportation, and energy system modeling. This paper analyzes this research portfolio, providing examples of individual use cases. The paper documents NREL's history of operating one of the world's most energy-efficient data centers while examining pathways to reduce economic and environmental impact beyond reduction of Power Usage Efficiency (PUE). This paper concludes by examining the unique opportunities created for accelerating improvements in data center efficiency created by combining an HPC system dedicated to energy research and a research program in energy-efficient computing.

cs.CY↗

arXiv2021

Energy use in quantum data centers: Scaling the impact of computer architecture, qubit performance, size, and thermal parameters

As quantum computers increase in size, the total energy used by a quantum data center, including the cooling, will become a greater concern. The cooling requirements of quantum computers, which must operate at temperatures near absolute zero, are determined by computing system parameters, including the number and type of physical qubits, the operating temperature, the packaging efficiency of the system, and the split between circuits operating at cryogenic temperatures and those operating at room temperature. When combined with thermal system parameters such as cooling efficiency and cryostat heat transfer, the total energy use can be determined. Using a first-principles energy model, this paper reports the impact of computer architecture and thermal parameters on the overall energy requirements. The results also show that power use and quantum volume can be analytically correlated. Approaches are identified for minimizing energy use in integrated quantum systems relative to computational power. The results show that the energy required for cooling is significantly larger than that required for computation, a reversal from energy usage patterns seen in conventional computing. Designing a sustainable quantum computer will require both efficient cooling and system design that minimizes cooling requirements.

quant-ph↗