January 21, 2021
Earlier this week (Jan. 19), HiPEAC — the European Network on High Performance and Embedded Architecture and Compilation — published the 8th edition of the HiPEAC Vision, detailing an increasingly interconnected computing landscape where complex tasks are carried out across multiple... Read more…
August 25, 2020
Larry Smarr may have stepped back from full-time work in the Computer Science and Engineering Department at the University of California, San Diego, but that do Read more…
March 16, 2020
For gamers, fighting against a global crisis is usually pure fantasy – but now, it’s looking more like a reality. As supercomputers around the world spin up Read more…
December 2, 2014
The ability to predict regional sea level changes over the next few decades takes on greater urgency as global carbon emissions continue to rise. The situation Read more…
August 18, 2014
Distributed computing has undergone many permutations, from its roots in grid computing to support large scientific endeavors to Sun-style utility computing, to Read more…
March 14, 2013
QMachine leverages the processing power of Web browsers to create a commodity supercomputer. Read more…
March 14, 2013
QMachine leverages the processing power of Web browsers to create a commodity supercomputer. Read more…
March 13, 2013
Quantum Cures wants your help identifying drug candidates for orphan and rare diseases. 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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