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…
November 14, 2023
In a few years, servers may not look the same as memory, storage, and accelerators move to separate enclosures. An interconnect called CXL is making that possib Read more…
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…
November 3, 2023
Intel's data center business is recovering, showing healthy margins despite growing competitive pressure. The healthy margins result from higher average se Read more…
September 10, 2023
The shortage of Nvidia's GPUs has customers searching for scrap heap to kickstart makeshift AI projects, and Intel is benefitting from it. Customers seeking qui Read more…
August 31, 2023
Supercomputing remains largely an on-premises affair for many reasons that include horsepower, security, and system management. Companies need more time to move Read more…
August 16, 2023
Google Cloud's new H3 virtual machine instances provide a big jump in performance thanks to a focus on network performance, but with restrictions: it only suppo Read more…
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…
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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