June 9, 2016
The old adage “you cannot improve what you do not measure" is fresh again in the age of ubiquitous data. When considering the challenges of exascale computing, power is right at the top of the list and the major leadership-class centers want to make sure they're doing everything they can to manage the demands of power today - which can run as high as 10 MW at peak for the largest machines - and in the coming exascale era, when the number could be three times that high. At loads of this magnitude, the largest HPC facilities need to have all the relevant power data within arm's reach. Read more…
February 19, 2016
Come early March, grants worth $300,000 are up for grabs for manufacturers giving year-long access to national lab supercomputing cycles and half the staff hour Read more…
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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