July 14, 2022
Harvard University is making a more concerted research computing push with the creation of a new university organization and a spate of hiring announcements. Th Read more…
October 9, 2021
X chromosome inactivation equalizes the active X chromosomes between mammals with two X chromosomes and mammals with one X and one Y chromosome – however, the Read more…
February 24, 2021
Last Thursday, a range of experts joined the Advanced Scale Forum (ASF) in a rapid-fire roundtable to discuss how advanced technologies have transformed the way humanity responded to the COVID-19 pandemic in indelible ways. The roundtable, held near the one-year mark of the first... Read more…
December 23, 2020
During SC20 in November, the HPCwire Readers’ and Editors’ Choice awards program celebrated its 17th year of honoring outstanding achievements in high-perfo Read more…
October 9, 2019
Harvard's Faculty of Arts & Sciences Research Computing (FASRC) center announced a refresh of their primary HPC resource. The new cluster, called Cannon after the pioneering American astronomer Annie Jump Cannon, is supplied by Lenovo... Read more…
April 11, 2014
Equal parts fascinating and confounding, the field of quantum computing keeps making headway. Two exciting developments are described in the current issue of Na 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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