Like many other HPC professionals I’m following the hype cycle around machine learning/deep learning with interest. I subscribe to the view that we’re probably approaching the ‘peak of infl …
Software Defined Visualization (SDVis), an open source initiative from Intel and industry collaborators, delivers breath-taking visual impact and interactivity for all scales of scientific and ph …
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Xiaoxiang Zhu, who works for the German Aerospace Center (DLR) and Technical University of Munich (TUM), was awarded the 2018 PRACE Ada Lovelace Award for HPC for her outstanding contributions in …
Sometime this month the U.S. House Science Committee will introduce legislation to launch a 10-year National Quantum Initiative, according to a recent report by The American Institute of Physics …
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Whether an organization chooses a cloud for general business needs or a highly tailored workload, the spectrum of offerings and configurations can be overwhelming. To help you navigate the various cloud options available today, we're breaking down your options, exploring pros and cons, and sharing ways to keep your options open and your business agile as you execute your cloud strategy.
Researchers in academic labs and commercial R&D groups continue to need more compute capacity, which means leveraging the latest innovations in HPC technologies as well as an assortment of resources to meet the unique needs of different workloads. Increasingly, systems based on Arm processors are stepping into that role, offering low power consumption and strategic advantages for HPC workloads.
Whether it's for fraud detection, personalized medicine, manufacturing, smart cities, autonomous vehicles and many other areas, advanced-scale computing has exploded beyond the realm of academia and government and into the private sector. And with data-intensive workloads on the rise, commercial users are turning to HPC-based infrastructure to run the AI, ML and cognitive computing applications that their organizations depend on.
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