In a properly working capitalist economy, innovative companies make big bets, help create new markets, vanquish competition or at least hold it at bay, and profit from all of the hard work, cleverness, luck, and deal making that comes with supplying a good or service to demanding customers. …
There are plenty of things that the members of the high performance community do not agree on, there is a growing consensus that machine learning applications will at least in some way be part of the workflow at HPC centers that do traditional simulation and modeling. …
One of the reasons why Nvidia has been able to quadruple revenues for its Tesla accelerators in recent quarters is that it doesn’t just sell raw accelerators as well as PCI-Express cards, but has become a system vendor in its own right through its DGX-1 server line. …
GPU computing has deep roots in supercomputing, but Nvidia is using that springboard to dive head first into the future of deep learning. …
Graphics chip maker Nvidia has taken more than a year and carefully and methodically transformed its GPUs into the compute engines for modern HPC, machine learning, and database workloads. …
When it comes to supercomputing, you don’t only have to strike while the iron is hot, you have to spend while the money is available. …
Over the last year in particular, we have documented the merger between high performance computing and deep learning and its various shared hardware and software ties. …
The future “Summit” pre-exascale supercomputer that is being built out in late 2017 and early 2018 for the US Department of Energy for its Oak Ridge National Laboratory looks like a giant cluster of systems that might be used for training neural networks. …
If you are trying to figure out what impact the new “Pascal” family of GPUs is going to have on the business at Nvidia, just take a gander at the recent financial results for the datacenter division of the company. …
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