January 26, 2023
The Atos-built, Nvidia SuperPod-based Berzelius supercomputer – housed in and operated by Sweden’s Linköping-based National Supercomputer Centre (NSC) – Read more…
October 17, 2022
Oregon State University (OSU) is planning to launch an expansive, expensive – $200 million – new research and education center. The center will be named aft Read more…
March 22, 2022
At GTC22 today, Nvidia unveiled its new H100 GPU, the first of its new ‘Hopper’ architecture, along with a slew of accompanying configurations, systems and Read more…
June 2, 2021
Nvidia is busy this week at the virtual Computex 2021 Taipei technology show, announcing an expansion of its nascent Nvidia-certified server program, a range of Read more…
April 12, 2021
At GTC 2021, Nvidia has announced an upgraded iteration of its DGX SuperPods, calling the new offering “the first cloud-native, multi-tenant supercomputer.” Read more…
Today, manufacturers of all sizes face many challenges. Not only do they need to deliver complex products quickly, they must do so with limited resources while continuously innovating and improving product quality. With the use of computer-aided engineering (CAE), engineers can design and test ideas for new products without having to physically build many expensive prototypes. This helps lower costs, enhance productivity, improve quality, and reduce time to market.
As the scale and scope of CAE grows, manufacturers need reliable partners with deep HPC and manufacturing expertise. Together with AMD, HPE provides a comprehensive portfolio of high performance systems and software, high value services, and an outstanding ecosystem of performance optimized CAE applications to help manufacturing customers reduce costs and improve quality, productivity, and time to market.
Read this whitepaper to learn how HPE and AMD set a new standard in CAE solutions for manufacturing and can help your organization optimize performance.
A workload-driven system capable of running HPC/AI workloads is more important than ever. Organizations face many challenges when building a system capable of running HPC and AI workloads. There are also many complexities in system design and integration. Building a workload driven solution requires expertise and domain knowledge that organizational staff may not possess.
This paper describes how Quanta Cloud Technology (QCT), a long-time Intel® partner, developed the Taiwania 2 and Taiwania 3 supercomputers to meet the research needs of the Taiwan’s academic, industrial, and enterprise users. The Taiwan National Center for High-Performance Computing (NCHC) selected QCT for their expertise in building HPC/AI supercomputers and providing worldwide end-to-end support for solutions from system design, through integration, benchmarking and installation for end users and system integrators to ensure customer success.
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