If you need a supercomputer for processing your topic maps, an affordable one is at hand.
Some assembly required. With Legos no less.
From the ScienceDigest post:
Computational Engineers at the University of Southampton have built a supercomputer from 64 Raspberry Pi computers and Lego.
The team, led by Professor Simon Cox, consisted of Richard Boardman, Andy Everett, Steven Johnston, Gereon Kaiping, Neil O’Brien, Mark Scott and Oz Parchment, along with Professor Cox’s son James Cox (aged 6) who provided specialist support on Lego and system testing.
Professor Cox comments: “As soon as we were able to source sufficient Raspberry Pi computers we wanted to see if it was possible to link them together into a supercomputer. We installed and built all of the necessary software on the Pi starting from a standard Debian Wheezy system image and we have published a guide so you can build your own supercomputer.”
The racking was built using Lego with a design developed by Simon and James, who has also been testing the Raspberry Pi by programming it using free computer programming software Python and Scratch over the summer. The machine, named “Iridis-Pi” after the University’s Iridis supercomputer, runs off a single 13 Amp mains socket and uses MPI (Message Passing Interface) to communicate between nodes using Ethernet. The whole system cost under £2,500 (excluding switches) and has a total of 64 processors and 1Tb of memory (16Gb SD cards for each Raspberry Pi). Professor Cox uses the free plug-in ‘Python Tools for Visual Studio’ to develop code for the Raspberry Pi.
You may also want to visit the Rasberry PI Foundation. Which has the slogan: “An ARM GNU/Linux box for $25. Take a byte!”
In an age with ready access to cloud computing resources, to say nothing of weapon quality toys (Playstation 3’s), for design simulations, there is still a place for inexpensive experimentation.
What hardware configurations will you test out on your Raspberry Pi Supercomputer?
Are there specialized configurations that work better for some subject identity tests than others?
How do hardware constraints influence our approaches to computational problems?
Are we missing solutions because they don’t fit current architectures and therefore aren’t considered? (Not rejected, just don’t come up at all.)
[…] See also: A Raspberry Pi Supercomputer […]
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