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Forget Zettascale, Trouble is Brewing in Scaling Exascale Supercomputers

November 14, 2023

In 2021, Intel famously declared its goal to get to zettascale supercomputing by 2027, or scaling today's Exascale computers by 1,000 times. Moving forward t Read more…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N Read more…

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Aurora the Survivor: Exascale Supercomputer Arrives After Eight Years of Doom

November 13, 2023

Many products were sacrificed in the eight years it took to bring the Aurora supercomputer to life. Nonetheless, anticipation for the second U.S.Exascale system Read more…

Top500: Aurora Is in the House, But It Is Still Frontier’s Party

November 13, 2023

The fall 2023 TOP500 list is out and Frontier retains its top spot and is still the only exascale machine. However, five new or upgraded systems have shaken up Read more…

EU Grabs ARM for First ExaFLOP Supercomputer, x86 Misses Out

October 4, 2023

The configuration of Europe's first exascale supercomputer, Jupiter, has been finalized, and it is a win for Nvidia and a disappointment for x86 chip vendors In Read more…

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Leveraging Machine Learning in Dark Matter Research for the Aurora Exascale System 

September 25, 2023

Scientists have unlocked many secrets about particle interactions at atomic and subatomic levels. However, one mystery that has eluded researchers is dark matte Read more…

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Argonne Places Aurora’s “Golden Blade” as Much Awaited Supercomputer Gathers Steam 

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…

Aurora’s 10,624 Blades in Place as Exascale System Nears Completion

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…

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Whitepaper

Penguin Computing Scyld Cloud Central™: A New Cloud-First Approach to HPC and AI Workloads

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.

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Sponsored by Penguin Solutions

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QCT POD- An Adaptive Converged Platform for HPC and AI

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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Sponsored by QCT

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