Researchers at ORNL and the University of Tennessee, Knoxville, demonstrated an AI-driven workflow that guided data collection in real time during a beamline experiment at Cornell University's CHESS facility.
This research demonstrates that applying custom, task-specific power limits to modern superchips is a highly effective strategy for saving significant amounts of GPU energy in high-performance computing.
High-performance computing systems consume vast amounts of energy, particularly when moving data between different parts of the machine. To address this challenge, a research team investigated a novel strategy for optimizing data transfers.
The Simplified Interface to Complex Memories (SICM) project delivers a powerful software solution that abstracts away the complexity of modern, multi-tiered memory systems.
The research team developed an intelligent, automated software solution that elegantly solves the complex problem of managing data in modern computers with multiple memory types.
Summary: Automation and autonomy can enable revolutionary scientific advances by coordinating a diverse array of experimental and computational capabilities more efficiently and more effectively than current hands-on approaches.
This research introduces a data-efficient, AI-driven framework for making smarter scheduling decisions in High-Performance Computing.
A study by researchers at ORNL traces a blueprint for a software architecture that would integrate emerging quantum computers with the world’s fastest supercomputing systems.
A multidisciplinary team of researchers from Oak Ridge National Laboratory (ORNL) and other institutions created a Machine Learning (ML) library for the training of classifiers on spectrographic chemical data.