December 5, 2023
IBM and Meta have co-launched a massive industry-academic-government alliance to shepherd AI development. The new group has united under the AI Alliance banner Read more…
August 8, 2023
A National Science Foundation webinar held last week and led by Bogdan Mihaila, director of NSF Physics at the Frontier program, provided more guidance for pote Read more…
July 27, 2023
Editor's note; The Day 1 and Day 2 reports from PEARC23 got crossed in the wires. Both reports are now posted. Thanks to Ken Chiacchia of the Pittsburgh Superco Read more…
July 18, 2023
NSF this week issued a solicitation for proposals to create a National Quantum Virtual Laboratory, which NSF describes as “an overarching shared infrastructur Read more…
May 10, 2023
A pattern is emerging on how U.S. government wants to boost its AI research. The approach is similar to how the early supercomputing infrastructures were built: Read more…
January 31, 2023
Last week the National AI Research Resource (NAIRR) Task Force released its final report and roadmap for building a national AI infrastructure to include comput Read more…
January 20, 2023
Security of high-performance computers is being neglected in the pursuit of horsepower, and there are concerns that the ignorance may be costly if safeguards ar Read more…
September 26, 2022
Since 2017, plans for the Leadership-Class Computing Facility (LCCF) have been underway. Slated for full operation somewhere around 2026, the LCCF’s scope ext 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.
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