Energy Systems Integration Partnerships, NREL + Hewlett-Packard
This fact sheet highlights work performed with Hewlett-Packard in the ESIF.
97 MATHEMATICS AND COMPUTING↗
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NREL's Building-Integrated Supercomputer Provides Heating and Efficient Computing (Fact Sheet)
NREL's Energy Systems Integration Facility (ESIF) is meant to investigate new ways to integrate energy sources so they work together efficiently, and one of the key tools to that investigation, a new supercomputer, is itself a prime example of energy systems integration. NREL teamed with Hewlett-Packard (HP) and Intel to develop the innovative warm-water, liquid-cooled Peregrine supercomputer, which not only operates efficiently but also serves as the primary source of building heat for ESIF offices and laboratories. This innovative high-performance computer (HPC) can perform more than a quadrillion calculations per second as part of the world's most energy-efficient HPC data center.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Molecular Quantum Mechanics 2010: From Methylene to DNA and Beyond Conference Support
This grant was $12500 for partial support of an international conference, Molecular Quantum Mechanics 2010, which was held on the campus of the University of California, Berkeley, from 24 to 29 May 2010. The conference involved more than 250 participants. The conference schedule ran from as early as 8:00 AM to as late as 10:30 PM at night, in order to accommodate six historical lectures, 16 plenary lectures, 42 invited talks and two very strong poster sessions containing 143 contributed posters. Since 1989, the Molecular Quantum Mechanics (MQM) series of international conferences has show- cased the frontiers of research in quantum chemistry with a strong focus on basic theory and algorithms, as well as highlights of topical applications. Both were strongly in evidence at MQM 2010. At the same time as embracing the future, the MQM conferences also honour the lifetime contributions of some of the most prominent scientists in the field of theoretical and computational quantum chemistry. MQM 2010 recognised the work of Prof. Henry F. ‘Fritz’ Schaefer of the Center for Computational Chemistry at the University of Georgia, who was previously on the faculty at Berkeley The travel of invited speakers was partially covered by sponsorships from Dell Computer, Hewlett-Packard, Journal of Chemical Theory and Computation, Virginia Tech College of Science, Molecular Physics, Q-Chem Inc and the American Institute of Physics. By contrast, the conference grant from the Department of Energy was used to provide fellowships and scholarships to enable graduate students and postdoctoral fellows to attend the meeting, and thereby broaden the participation of young scientists at a meeting where in the past most of the attendees have been more senior faculty researchers. We believe that we were very successful in this regard: 118 students and postdocs attended out of the total of 256 participants. In detail, the DOE sponsorship money was partially used for dormitory scholarships that covered the cost of shared accommodation for students and postdocs at Berkeley dormitories. This covered the $200-$305 cost of a shared room for the 5-day duration of the conference. The only condition of these scholarships was that the awardee must present a poster at the meeting. Approximately $7565 was spent for these dormitory scholarships. The remaining expenditures of $4800 was used for 12 merit scholarships which were awarded to students whose poster presentations were judged the best at the conference. This amount covered a significant part of their travel and registration fees.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Artificial Intelligence for Data Center Operations (AI Ops)
HPC data centers such as the one at NREL's ESIF will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence (AI) and machine learning (ML) approaches provide the means to improve HPC data center efficiency (energy, operational, and managerial efficiency) and resiliency by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. The goal of coupled improvement of data center resiliency and energy efficiency through automated data collection and AI has led to a multi-year, multi-staged collaboration between NREL and Hewlett-Packard Enterprise's Advanced Technology Group, referred to as Artificial Intelligence for Data Center Operations (AIOps). The extended efforts within the AIOps project include a common goal of building capabilities for an advanced smart facility and demonstration of data collection and AI modeling techniques in the ESIF data center.
97 MATHEMATICS AND COMPUTING↗
Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments
Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.
Saranathan, Gayathri [Hewlett-Packard]↗
Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)
High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.
97 MATHEMATICS AND COMPUTING↗
LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers
Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab's Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL agents optimize critical thermal controls like liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We benchmark centralized and decentralized multi-agent RL approaches, demonstrate policy distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions.
Naug, Avisek [Hewlett Packard Enterprise]↗