June 8, 2017
The ways that advanced computing performance depends on more – much more – than the processor take many forms. Regardless of Moore’s Law validity, it’s Read more…
April 6, 2015
Let's talk about parallel loops. In parallel computing, we've been designing, describing, implementing and using parallel loops almost since the beginning. Th Read more…
March 12, 2015
Paul Messina, director of science for the Argonne Leadership Computing Facility (ALCF), discusses the primary objectives, curriculum and importance of the Argon Read more…
March 2, 2015
The high performance computing (HPC) community is heading toward the era of exascale machines, expected to exhibit an unprecedented level of complexity and size 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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