November 21, 2023
Since 2007, the Student Cluster Competition (SCC) has provided an international multi-day contest for the best and brightest university HPC teams. This year, th Read more…
October 12, 2023
A recent article on Tom's Hardware began with the headline "China Wants 300 ExaFLOPS of Compute Power by 2025." Intrigued, further reading finds the following l Read more…
July 17, 2023
Editor’s note; Recently Google's Bard provided what it considered the Top 5 Trends in HPC for the First 6 Months of 2023 to HPCwire. While the answers Read more…
July 12, 2023
Editor’s note; In light of recent updates to Google’s Privacy Policy, “For example, we use publicly available information to help train Google’s AI mo Read more…
June 28, 2023
As promised, MLCommons added a large language model (based on GPT-3) to its MLPerf training suite (v3.0) and released the latest round of results yesterday. Onl Read more…
June 7, 2023
Perhaps the most interesting slide at Hyperion Research’s annual ISC breakfast HPC market update was one without numbers, presented by research director Mark Read more…
May 1, 2023
In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the high-performance computing community. Whether it� Read more…
April 5, 2023
MLCommons today released the latest MLPerf Inferencing (v3.0) results for the datacenter and edge. While Nvidia continues to dominate the results – topping al Read more…
Making the Most of Today’s Cloud-First Approach to Running HPC and AI Workloads With Penguin Scyld Cloud Central™
Bursting to cloud has long been used to complement on-premises HPC capacity to meet variable compute demands. But in today’s age of cloud, many workloads start on the cloud with little IT or corporate oversight. What is needed is a way to operationalize the use of these cloud resources so that users get the compute power they need when they need it, but with constraints that take costs and the efficient use of existing compute power into account. Download this special report to learn more about this topic.
Data center infrastructure running AI and HPC workloads requires powerful microprocessor chips and the use of CPUs, GPUs, and acceleration chips to carry out compute intensive tasks. AI and HPC processing generate excessive heat which results in higher data center power consumption and additional data center costs.
Data centers traditionally use air cooling solutions including heatsinks and fans that may not be able to reduce energy consumption while maintaining infrastructure performance for AI and HPC workloads. Liquid cooled systems will be increasingly replacing air cooled solutions for data centers running HPC and AI workloads to meet heat and performance needs.
QCT worked with Intel to develop the QCT QoolRack, a rack-level direct-to-chip cooling solution which meets data center needs with impressive cooling power savings per rack over air cooled solutions, and reduces data centers’ carbon footprint with QCT QoolRack smart management.
© 2023 HPCwire. All Rights Reserved. A Tabor Communications Publication
HPCwire is a registered trademark of Tabor Communications, Inc. Use of this site is governed by our Terms of Use and Privacy Policy.
Reproduction in whole or in part in any form or medium without express written permission of Tabor Communications, Inc. is prohibited.