Say hi to Parallella, the $99 Linux-powered supercomputer. (Image: The Linux Foundation)
Posted on 04/16/2013 10:50:04 AM PDT by ShadowAce
Chip-company Adapteva announced on April 15th at the Linux Collaboration Summit in San Francisco, California, that they've built their first Parallella parallel-processing board for Linux supercomputing, and that they'll be sending them to their 6,300 Kickstarter supporters and other customers by this summer.
Linux has long been the number one supercomputer operating system. But while you could build your own Linux supercomputer using commercial off-the-shelf (COTS) products, it wouldn't be terribly fast. You needed hardware that could support massively parallel computing — the cornerstone of modern supercomputing.
What Adapteva has done is create a credit-card sized parallel-processing board. This comes with a dual-core ARM A9 processor and a 64-core Epiphany Multicore Accelerator chip, along with 1GB of RAM, a microSD card, two USB 2.0 ports, 10/100/1000 Ethernet, and an HDMI connection. If all goes well, by itself, this board should deliver about 90 GFLOPS of performance, or — in terms PC users understand — about the same horse-power as a 45GHz CPU.
This board will use Ubuntu Linux 12.04 for its operating system. To put all this to work, the platform reference design and drivers are now available.
Why would you want a $99 supercomputer?
Well, besides the fact that it would be really cool, Adapteva CEO Andreas Olofsson explained:
Historically, serial processing [conventional computing] improved so quickly that in most applications, there was no need for massively parallel processing. Unfortunately, serial processing performance has now hit a brick wall, and the only practical path to scaling performance in the future is through parallel processing. To make parallel software applications ubiquitous, we will need to make parallel hardware accessible to all programmers, create much more productive parallel programming methods, and convert all serial programmers to parallel programmers.
And of course, Olofsson added, to "make parallel computing accessible to everyone so we can speed up the adoption of parallel processing in the industry", the Parallella had to be created. Olofsson admitted that his company couldn't have done it by itself. The project required, and got, the support of other hardware OEMs, including Xilinx, Analog Devices, Intersil, Micron, Microchip, and Samtec. The companies have enabled Adapteva to bring its first per-production boards to San Francisco, and soon, to its eager programmer customers.
And to us know-nothings, what does this mean ??
Parallel computing isn’t of much use to most ordinary folks. It’s great if you want so simulate a nuclear explosion or predict the weather 10 days from now or do finite element analysis of a new airplane wing design.
I believe you’re wrong. With the advent of multi-core systems, GPUs and the software to take advantage of them (games, video editing, etc), parallel computing is here.
Anyone using C# “await()” functionality yet?
This is really cool...!
Might be fun to run Folding@Home on this little guy....
To me it means things are interesting again and I’ll start playing around in the 1’s & 0’s world again
Bump for later
Faster and cheaper computers. Without Windoze to slow it down.
Imagine Soldworks, (CAD) a Fast FEA ( Cosmo's is it still around?) and Virtual Gibbs CAM on a very cheap and fast box....
An inventor's or arm chair aero-engineers dream...
In a world without walls, who needs windows?
Can I predict global warming with this rig?
It would be nice to run molecular binding energy computations in minutes instead of days.
Very fast Solitaire!
Mark
Thats an impressive piece of hardware for $99. I dig those Zynqs. They have some shortcomings, but they’re nice devices overall. It’s always nice to see an FPGA related post here on FR :)!
It’s useful anywhere where you have a large number of generally independent calculations going on at the same time. Graphics (including image/video processing and editing) should really eat this up.
Or in geek speak, it turns O(n) calculations into O(1) calculations.
We’ve all had heavy duty parallel processing power in our PC’s for quite a while... they’re called GPU’s. Video processing does not require tons of speed, just lots of throughput - which is the perfect problem for parallel processing to solve. Modern GPUs have application programming interfaces which can be harnessed for parallel processing needs and for certain types of work a gaming class GPU can crunch numbers much more quickly than a x64 processor.
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