Intel’s new Xeon Phi, codenamed Knights Landing, is now shipping to developers in two different systems. Will the advent of manycore architectures finally start yielding multi-threading improvements for common users, or is this type of performance intrinsically limited to high-end scientific computing?
It is starting to seem like with fast enough hardware, nearly any repetitious task can be learned by a computer or robot. Nvidia is working to make sure that whenever it happens, it’s powered by Nvidia.
The DoE is partnering with a group of diverse American firms to share supercomputing time and find ways to optimize manufacturing and design processes. If the program works, it could be the beginning of a new type of performance scaling based on expanding shared knowledge rather than transistor frequency.
Deep learning is clearly the hottest technology driving the rapid advances in self-driving cars. Nvidia aims to make it practical, and enable the deployment of commercially-viable fleets of cars that can not just see, but navigate, for themselves.
Google has been working with its quantum computer for several years now, and finally has results that prove the D-Wave 2 can perform certain calculations up to a hundred million times faster than existing conventional computers. That kind of performance gain is only possible if the system is actually performing quantum computing.
It’s that time of the year again: TOP500 published its twice-annual list of the 500 fastest supercomputers in the world today, and it looks like China’s Tianhe-2 is still on top — and for the sixth consecutive time).
While the US has plenty of representation in the current TOP500 list of the fastest supercomputers in the world, it lost the #1 slot to China’s Tianhe-2 several years ago. President Obama wants to fix that immediately.
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