May 31, 2023
The end goal is in sight for the multi-institutional Exascale Computing Project (ECP), which launched in 2016 with a mandate from the Department of Energy (DOE) Read more…
February 28, 2023
Enabling interoperability across U.S. exascale supercomputers is one of the chief goals for the U.S. Exascale Computing Project (ECP), which has broadly oversee Read more…
July 15, 2022
The development of a whole device model (WDM) for a fusion reactor is critical for the science of magnetically confined fusion plasmas. In the next decade, the Read more…
July 6, 2022
In this one-on-one interview, Doug Kothe – associate laboratory director, Computing and Computational Sciences at Oak Ridge National Laboratory, and director Read more…
March 2, 2022
High-performance computing, or supercomputing, combined with new data-science approaches such as machine learning and artificial intelligence (AI) give scientis Read more…
February 22, 2022
Message Passing Interface (MPI) has been the communications backbone for distributed high-performance computing (HPC) scientific applications since its introduc Read more…
October 8, 2021
It is well known in the high-performance computing (HPC) community that many (perhaps most) HPC workloads exhibit dynamic performance envelopes that can stress Read more…
July 30, 2021
The ExaSky project, one of the critical Earth and Space Science applications being solved by the US Department of Energy’s (DOE’s) Exascale Computing Project (ECP), is preparing to use the nation’s forthcoming exascale supercomputers. Exascale machines will enable the ExaSky team to verify the gravitational influences, gas dynamics, and astrophysical inputs that they use to model the universe... 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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