April 6, 2020
A wide range of supercomputers are crunching the infamous “spike” protein of the novel coronavirus, from Summit more than a month ago to [email protected] to a Russian cluster just a week ago. Read more…
February 14, 2018
Last week, Dan Coats, the director of Director of National Intelligence for the U.S., warned the Senate Intelligence Committee that Russia was likely to meddle in the 2018 mid-term U.S. elections, much as it stands accused of doing in the 2016 Presidential election. Read more…
September 21, 2017
Brookhaven National Laboratory announced today that Adolfy Hoisie will chair its newly formed Computing for National Security department, which is part of Brook Read more…
October 3, 2016
Efforts to emulate signaling produced at nerve cell synapses aren’t new. Many different approaches – CMOS circuits and ‘ionic-drift’ based memristor technology, for example – have been tried, all with various shortcomings. Last week, researchers from UMass, Loughborough University, Hewlett Packard Labs, and Brookhaven National Laboratory reported a new approach that closely mimics the Ca2+ diffusion dynamics that occur at synapses Read more…
January 7, 2016
Barbara Chapman, a leading researcher in programming languages, programming models, and compilers, has been named head of the Computer Science and Mathematics G Read more…
October 1, 2013
It's one of the most essential questions, which speaks to the very fact of our existence: why is the universe made of matter? Read more…
Five Recommendations to Optimize Data Pipelines
When building AI systems at scale, managing the flow of data can make or break a business. The various stages of the AI data pipeline pose unique challenges that can disrupt or misdirect the flow of data, ultimately impacting the effectiveness of AI storage and systems.
With so many applications and diverse requirements for data types, management systems, workloads, and compliance regulations, these challenges are only amplified. Without a clear, continuous flow of data throughout the AI data lifecycle, AI models can perform poorly or even dangerously.
To ensure your AI systems are optimized, follow these five essential steps to eliminate bottlenecks and maximize efficiency.
Karlsruhe Institute of Technology (KIT) is an elite public research university located in Karlsruhe, Germany and is engaged in a broad range of disciplines in natural sciences, engineering, economics, humanities, and social sciences. For institutions like KIT, HPC has become indispensable to cutting-edge research in these areas.
KIT’s HoreKa supercomputer supports hundreds of research initiatives including a project aimed at predicting when the Earth’s ozone layer will be fully healed. With HoreKa, projects like these can process larger amounts of data enabling researchers to deepen their understanding of highly complex natural processes.
Read this case study to learn how KIT implemented their supercomputer powered by Lenovo ThinkSystem servers, featuring Lenovo Neptune™ liquid cooling technology, to attain higher performance while reducing power consumption.
© 2023 HPCwire. All Rights Reserved. A Tabor Communications Publication
Reproduction in whole or in part in any form or medium without express written permission of Tabor Communications, Inc. is prohibited.