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.
Scientists at ORNL have created a new method that more than doubles computer processing speeds while using 75 percent less memory to analyze plant imaging data.
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 innovative Celeritas project, led by ORNL, provides a software tool that makes sure simulations used to analyze particles can run on the fastest supercomputers, accelerating answers about the nature of the universe.
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.