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The Present and Future of AI: A Discussion with HPC Visionary Dr. Eng Lim Goh
November 27, 2020
As HPE’s chief technology officer for artificial intelligence, Dr. Eng Lim Goh devotes much of his time talking and consulting with enterprise customers about Read more…
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Penguin Computing Scyld Cloud Central™: A New Cloud-First Approach to HPC and AI Workloads
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.
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QCT POD- An Adaptive Converged Platform for HPC and AI
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.
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