Long time readers know we have a serious interest in screaming fast computers and try to get to the Top500 list a couple times a year. Here is a computer that was at the top of that list, the fastest computer in the world just four years ago. And it's being shut down.That was from a 2013 post.
Technology changes pretty fast.
Among the fastest processors in the business are the one's originally developed for video games and known as Graphics Processing Units or GPU's. Since Nvidia released their Tesla hardware in 2008 hobbyists (and others) have used GPU's to build personal supercomputers.
Here's Nvidias Build your Own page.
Or have your tech guy build one for you.
In addition Nvidia has very fast connectors they call NVLink.
Using a hybrid combination of IBM Central Processing Units (CPU's) and Nvidia's GPU's, all hooked together with the NVLink, Oak Ridge National Laboratory is building what will be the world's fastest supercomputer when it debuts in 2018.
As your kid plays Grand Theft Auto.
From IEEE Spectrum, April 9, 2015:
Nvidia Wants to Build the Robocar's Brain
Nvidia, the graphic-card master, wants to do for self-driving cars what it’s done for gaming and supercomputing. It wants to supply the hardware core—the automotive brain onto which others can build their applications.*Here's the Top 500 site, the next list is due next month. China’s National University of Defense Technology has had the top spot since the June 2013 list when it toppled Oak Ridge National Laboratory's Titan.
It’s called Drive PX, and next month it will be released to auto makers and top-tier suppliers for US $10,000 a pop (that’s a development kit—future commercial versions will cost far less). It packs a pair of the company’s Tegra X1 processors, each capable of a bit more than a teraflop—a trillion floating-point operations per second. Together they can manage up to 12 cameras, including units that monitor the driver for things like drowsiness or distractedness. “Sensor fusion,” which puts the various streams of data into a single picture, can even include input from radar and its laser-ranging equivalent, lidar. The result is the ability to recognize cars, pedestrians and street signs.
If you’ve played Grand Theft Auto, you’ll have a good idea of what a professional driving simulator is like, and if you’ve played with simulators, you’ll have a passing familiarity with self-driving cars. These systems manage parallel streams of visual data—and parallel processing is what Nvidia’s graphics processing units, or GPUs, are designed for.
Until now, the main non-gamelike application for GPUs has been in supercomputers, which also bears on the self-driving problem, where it’s important to dive into huge databases in order to learn from experience. Nvidia calls this its “deep learning” project.
“The majority of top supercomputers use Nvidia GPUs, including Titan, the largest in the U.S.,” notes Michael Houston, the technical lead for the project. “Deep learning has different applications. The focus has been on the visual analysis of imaging in video—web science, embedded systems and automotive. Fundamentally, we’re processing pixels.”
Learning as you go would be the ideal experimental method, and such a skill would come in handy whenever the high-detail maps on which autonomous cars rely fail—for instance, when a truck jackknifes, closing a lane. Right now, though, safety regulators take a dim view of such cybernetic self-assertion, so anything a car learns must first be uploaded to the cloud for analysis offline. Only later can the car get the lesson via software updates.
Auto companies that work with Nvidia (which, by the way, already has processors of one kind or another in some 8 million cars) and are presumed to be lining up for the development kit include Tesla, Audi and BMW, as well as top-tier suppliers, such as Delphi. These companies will build their own systems on top of the Nvidia framework....MORE
Here's ORNL's webpage for the new supercomputer, Summit.