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…
September 20, 2016
Throughout the past year, the National Center for Supercomputing Applications has been celebrating its 30th anniversary. On Friday, Larry Smarr, whose unsolicited 1983 proposal to the National Science Foundation (NSF) begat NCSA in 1985 and helped spur NSF to create not one but five national centers for supercomputing, gave a celebratory talk at NCSA. Read more…
February 12, 2016
By now you’ve likely heard that scientists reported detecting the long-sought gravitational waves; this is roughly a 100 years since their prediction by Einst Read more…
September 7, 2010
The naming of Michael Norman as director of the San Diego Supercomputer Center (SDSC) last week was long overdue. SDSC has been without an official director for more than 14 months, with Norman filling the spot as the interim head since last July. The appointment could mark something of a comeback for the center, which has not only gone director-less during this time, but has been operating without a high-end supercomputer as well. Read more…
January 4, 2010
In a position paper for community input at NSF's Future of High Performance Computing Workshop in early December, Calit2 Director Larry Smarr reviewed the successes, failures and continuing challenges of the NSF supercomputing program that he helped create. 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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