Presentation
The Computational and Economic Breakeven of Hybrid HPC/ML
DescriptionHigh performance computing is rapidly integrating machine learning with traditional numerical solvers, creating hybrid workflows and digital twins. This panel audits the "total energy to solution" for hybrid HPC workloads, quantifying their operational power demands from silicon hardware to total cost of ownership. Panelists will address the following core questions: What is the energy required to generate large-scale, ground-truth training datasets versus the direct computational cost of ML training? What, if any, is the energy overhead of running traditional physics engines alongside ML for active validation? At what computational breakeven point does deploying an ML surrogate yield a net reduction in total energy use? Finally, how do these physical energy ledgers translate into real-world budgets and sustainability goals for scientific deployment? Bringing together perspectives from materials science, weather forecasting, hyperscale computing, and computing economics, this panel evaluates the physical and financial energy realities of ML integration in HPC.
Event Type
Panel
TimeWednesday, 18 November 20263:30pm - 5:00pm CST
LocationW185abc
TP