Publications
Showing 24 results for Author: Wes H. Brewer
Aug, 2026
Conference Paper
SetGo: Metadata Readiness for Scientific AI Datasets
Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse. The Readiness Engine for Data Integration (REDI) addresses computational readiness, but no corresponding tool evaluates whether a dataset’s metadata are sufficiently complete, governed, and standards-compliant for publica…
May, 2026
Journal
Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials
This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain-specific constraints. We introduce a two-dimensional readiness model that…
May, 2026
Conference Paper
Data Readiness for Scientific AI at Scale
This paper examines how Data Readiness for AI (DRAI) principles apply to leadership-scale scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, bio/health, and materials—to identify common preprocessing patterns and domain-specific constraints. We introduce a two-dimensional readines…
May, 2026
Conference Paper
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…
Apr, 2026
Journal
Mixed-precision numerics in scientific applications: survey and perspectives
The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision capabilities, which can achieve performance improvements of up to 8 compared to double-precision in extreme compute-intensive workloads, re…
Mar, 2026
Conference Paper
Visualizing an Exascale Data Center Digital Twin: Considerations, Challenges and Opportunities
Digital twins are an excellent tool to model, visualize, and simulate complex systems, to understand and optimize their operation. In this work, we present the technical challenges of real-time visualization of a digital twin of the Frontier supercomputer.We show the initial prototype and current state of the twin and highlight technical design challenges of visualizing su…
Mar, 2026
Conference Paper
Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training
With the end of Moore’s law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intelligent subsampling? To explore this, we develop SICKLE, a sparse intelligent curation framework for efficient learning, featuring a novel maximum entropy (MaxEnt) sampling approach, scalable train…
Mar, 2026
Conference Paper
Dynamic Modeling of Power Conversion Stages for an Exascale Supercomputer
In this paper a power conversion and energy consumption model for an exascale supercomputer is investigated. Power consumption, energy loss and efficiency are derived for the 27.2 MW liquid-cooled, centralized, High Performance Computing (HPC) power system, which is supplied directly from the 480 V three-phase mains. Two energy conversion stages are analyzed, measured and…
Mar, 2026
Conference Paper
HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling
Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate sched- ulers are limited to post-deployment analysis, or simulators, which do not model associated infrastructure. In this work, we present the first-of-its-kind integration of scheduling and digital twins in HPC. This enables what-if studies to understan…
Mar, 2026
Conference Paper
In-Transit Data Transport Strategies for Coupled AI-Simulation Workflow Patterns
Coupled AI-Simulation workflows are becoming the major workloads for HPC facilities, and their increasing complexity necessitates new tools for performance analysis and prototyping of new in-situ workflows. We present SimAI-Bench, a tool designed to both prototype and evaluate these coupled workflows. In this paper, we use SimAI-Bench to benchmark the data transport perfor…
Mar, 2026
Conference Paper
A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale
We present ExaDigiT, an open-source framework for developing comprehensive digital twins of liquid-cooled supercomputers. It integrates three main modules: (1) a resource allocator and power simulator, (2) a transient thermo-fluidic cooling model, and (3) an augmented reality model of the supercomputer and central energy plant. The framework enables the study of "what-if"…