November 17, 2022
For three years running, ACM has awarded not only its long-standing Gordon Bell Prize (read more about this year’s winner here!) but also its Gordon Bell Spec Read more…
September 29, 2022
Two DOE-funded projects — and a bunch of top supercomputers — are converging to improve our understanding of earthquakes and enable the construction of more Read more…
April 28, 2022
Roughly a year ago the National Energy Research Scientific Computing Center (NERSC) launched Perlmutter, which was hailed at the time as the “world’s fastest AI supercomputer” by Nvidia whose GPUs provide much of Perlmutter’s power. Since then, NERSC has been aggressively ramping up its mixed AI-HPC workload capability – software, early science apps... Read more…
June 28, 2021
The 57th Top500, revealed today from the ISC 2021 digital event, showcases many of the same systems as the previous edition, with Fugaku holding its significant lead and only one new entrant in the top 10 cohort: the Perlmutter system at the DOE Lawrence Berkeley National Laboratory enters the list at number five with 65.69 Linpack petaflops. Perlmutter is the largest... Read more…
May 27, 2021
A ribbon-cutting ceremony held virtually at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC) today marked the official launch of Perlmutter – aka NERSC-9 – the GPU-accelerated supercomputer built by HPE in partnership with Nvidia and AMD. Read more…
August 20, 2020
Nvidia has announced its Q2 2021 earnings: $3.87 billion, outperforming analyst expectations amid a global pandemic and showing signs of a successful (and ongoing) reorientation of its business strategy. Compared to Q2 2020, earnings were up 50 percent; compared to Q1 2021, up 26 percent. 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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