August 16, 2010
Cloud infrastructure is still lucrative if comparing its economics to building in-house HPC machines. However, cloud for HPC has to be efficient enough to reach proper performance ceilings without disappointing customers who probably experienced at a certain point to run their HPC applications on dedicated machines. As part of an ongoing series, Dr. Mohamed F. Ahmed addresses the multitude of challenges inherent to porting HPC applications in the cloud and what steps need to be taken in research to address such barriers. 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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