It has happened time and time again in computing in the past three decades in the datacenter: A device scales up its capacity – be it compute, storage, or networking – as high as it can go, and then it has to go parallel and scale out. …
The dark and mysterious art of artificial intelligence and machine learning is neither straightforward, or easy. …
Neural networks live on data and rely on computational firepower to help them take in that data, train on it and learn from it. …
There is increasing pressure in such fields as manufacturing, energy and transportation to adopt AI and machine learning to help improve efficiencies in operations, optimize workflows, enhance business decisions through analytics and reduce costs in logistics. …
Although oil and gas software giant, Baker Hughes, is not in the business of high performance computing, the software it creates for the world’s leading oil and gas companies requires supercomputing capabilities for some use cases and increasingly, these systems can serve double-duty for emerging deep learning workloads. …
Almost two years after the acquisition by Intel, the deep learning chip architecture from startup Nervana Systems will finally be moving from its codenamed “Lake Crest” status to an actual product. …
The differences between peak theoretical computing capacity of a system and the actual performance it delivers can be stark. …
Generally speaking, the world’s largest chip makers have been pretty secretive about the giant supercomputers they use to design and test their devices, although occasionally, Intel and AMD have provided some insight into their clusters. …
Medical imaging is one areas where hospitals have invested significantly in on-premises infrastructure to support diagnostic analysis. …
All the shiny and zippy hardware in the world is meaningless without software, and that software can only go mainstream if it is easy to use. …
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