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…
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…
June 22, 2023
AWS has finally made available its Arm-based CPUs available for supercomputing – but it's not a chip you can buy off the shelf. The chip, Graviton3E, is acces Read more…
December 2, 2022
The race to ever-better flops-per-watt and power usage effectiveness (PUE) has, historically, dominated the conversation over sustainability in HPC – but at S Read more…
November 17, 2022
At the awards ceremony at SC22 in Dallas today, ACM awarded the 2022 ACM Gordon Bell Prize to a team of researchers who used four major supercomputers – inclu Read more…
October 13, 2022
Two years have passed since the debut of Fugaku, Japan's top supercomputer, and it is already known for its numerous achievements in simulations for Covid-19 measures. Meanwhile, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) has already started to consider... 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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