October 3, 2023
The steady research into developing real-world applications for quantum computing is piling up interesting use cases. Today, IBM reported on work with Boeing to 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…
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…
May 12, 2023
Travis Humble is the director the Quantum Science Center (QSC) at Oak Ridge National Laboratory. QSC is one of six National QIS Research established by the U.S. National Quantum Initiative Act (NQIA) in 2018 and being overseen by the Department of Energy. Hopes are high that these centers, through their own research and in collaboration... Read more…
May 9, 2023
Yesterday, IBM announced its newest quantum processor – Osprey – introduced last December is now accessible as an “as an exploratory technical demonstrati Read more…
May 2, 2023
A fascinating ACM paper by researchers Torsten Hoefler (ETH Zurich), Thomas Häner (Microsoft*) and Matthias Troyer (Microsoft) – "Disentangling Hype from Pra 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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