July 24, 2023
While not a golden HPC spike, the final blade has been loaded into Aurora. As mentioned previously, final preparation of Aurora is underway. Aurora the "almost Read more…
July 20, 2023
Hyping an AI chip is one thing, but proving its usability in the commercial market is a bigger challenge. Some AI chip companies -- which are still prov Read more…
June 22, 2023
Aurora, one of the first three U.S. exascale supercomputers, has not had a straightforward path to installation and operation. The system has been repeatedly re Read more…
April 18, 2023
Weather and climate applications are some of the most important for high-performance computing, often serving as raisons d'être and flagship workloads for the Read more…
March 2, 2023
Researchers have been working for many decades to convert abundant, renewable biomass – e.g. crops and algae – into viable alternatives to nonrenewable, dir Read more…
January 19, 2023
Traditional utility planning based on more or less stable seasons year-over-year isn’t cutting it any more – and supercomputing is key to helping utilities Read more…
November 17, 2022
For three years running, ACM has awarded not only its long-standing Gordon Bell Prize (read more about this year’s winner here!) but also its Gordon Bell Spec Read more…
November 17, 2022
Large language models (LLMs) have taken the tech world by storm over the past couple of years, dominating headlines with their ability to generate convincing hu 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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