April 4, 2016
Lipid molecules are schizophrenic. One end likes to hang out with a charged crowd (think water); the other prefers neutral neighbors (think fats). Most of us re Read more…
October 14, 2015
A team of researchers from Brown University, ETH Zurich, the Universita da Svizzera Italiana (USI) and Consiglio Nazionale delle Ricerche (CNR) is using Americ Read more…
April 14, 2014
If we're out of "magic bullets" that can shoot across supercomputing space, shattering assumptions about how high performance computing operates efficiently at Read more…
March 27, 2014
The GPU Technology Conference this week in San Jose offered plenty of material for the supercomputing set with a number of presentations focused on specific pro 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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