Cray Supercomputing as a Service Becomes a Reality
For a mature company that kickstarted supercomputing as we know it, Cray has done a rather impressive job of reinventing itself over the years. …
For a mature company that kickstarted supercomputing as we know it, Cray has done a rather impressive job of reinventing itself over the years. …
After years of planning and delays after a massive architectural change, the Blue Waters supercomputer at the National Center for Supercomputing Applications at the University of Illinois finally went into production in 2013, giving scientists, engineers and researchers across the country a powerful tool to run and solve the most complex and challenging applications in a broad range of scientific areas, from astrophysics and neuroscience to biophysics and molecular research. …
It is hard to tell which part of the systems market is lumpier – that for traditional HPC systems like supercomputers or that for massive cluster deployments for the hyperscalers that run public clouds and public facing applications on a massive scale. …
Over the course of the last five years, GPU computing has featured prominently in supercomputing as an accelerator on some of the world’s fastest machines. …
Bad things sometimes happen to good companies, but the great ones are resilient; they ride out the difficulties and keep forging ahead. …
Having access to fairly reliable 10-day forecasts is a luxury, but it comes with high computational costs for centers in the business of providing predictability. …
For those in enterprise circles who still conjure black and white images of hulking supercomputers when they hear the name “Cray,” it is worth noting that the long-standing company has done a rather successful job of shifting a critical side of its business to graph analytics and large-scale data processing. …
Supercomputer maker Cray might not roll out machines for deep learning anytime in 2016, but like other system vendors with deep roots in high performance computing, which leverages many of the same hardware elements (strong interconnect and GPU acceleration, among others), they are seeing how to loop their expertise into a future where machine learning rules. …
Not every organization that relies on supercomputers can replace a whole machine in one fell swoop. …
The bottlenecks never get removed from a system, they just shift around as you change one component or the other. …
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