May 30, 2022
During a special address at ISC today, general manager and vice president of Accelerated Computing at Nvidia, Ian Buck, shared promising news for the future of Read more…
June 22, 2021
Making machine learning easier to use is all the rage in enterprise computing as more and more businesses are finding out that adopting and integrating AI is harder to do than they first anticipated. HPE, like other large technology vendors, has been hearing that message from customers and in response, has acquired Determined AI, a San Francisco... Read more…
July 27, 2020
Recurrent neural networks (RNNs) have shown phenomenal success in several sequence learning tasks such as machine translation, language processing, image captio Read more…
June 22, 2020
Nvidia unveiled its Selene AI supercomputer today in tandem with the updated listing of world’s fastest computers. Nvidia also introduced the PCIe form factor of the Ampere-based A100 GPU. Nvidia’s new internal AI supercomputer, Selene, joins the upper echelon of the 55th Top500’s ranks and breaks an energy-efficiency... Read more…
May 27, 2020
IBM researchers have taken another step towards making in-memory computing based on phase change (PCM) memory devices a reality. Papers in Nature and Frontiers 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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