August 1, 2023
Quantum computing is in the midst of the so-called NISQ era – a time of noisy intermediate scale quantum devices based a variety of qubit modalities, all of w 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 15, 2023
Today, Intel announced its first silicon spin-based qubit quantum chip – Tunnel Falls – a 12-qubit research chip and a program intended to selectively share Read more…
June 3, 2023
Researchers are leveraging photonics to develop and scale the hardware necessary to tackle the stringent requirements of quantum information technologies. By ex Read more…
June 2, 2023
HPCwire Person to Watch Marco Pistoia wears a lot of hats at JPMorgan Chase & Co.: managing director, distinguished engineer, head of global technology appl Read more…
May 23, 2023
Europe has clearly jumped into the global race to achieve practical quantum, though perhaps a step later (by a year or two) than the U.S. and China. Impressivel Read more…
May 21, 2023
At ISC this week, Nvidia announced plans for a new hybrid classical-quantum computing lab with partners Jülich Supercomputing Centre and ParTec. The new lab is 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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