4 papers
Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training
Wesley Brewer, Murali Meena Gopalakrishnan, Matthias Maiterth +12
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 intell…
Trace Replay Simulation of MIT SuperCloud for Studying Optimal Sustainability Policies
Wesley Brewer, Matthias Maiterth, Damien Fay
The rapid growth of AI supercomputing is creating unprecedented power demands, with next-generation GPU datacenters requiring hundreds of megawatts and producing fast, large swings…
HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling
Matthias Maiterth, Wesley H. Brewer, Jaya S. Kuruvella +8
Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate schedulers are limited to post-deployment analysis, or simul…
A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale
Wesley Brewer, Matthias Maiterth, Vineet Kumar +8
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…