Super Computing with Mini ITX Cluster

[Colin Alston] was able to snag a handful of Mini ITX motherboards for cheap and built a mini super computer he calls TinyJaguar. Named partly after the AMD Sempron 2650 APU, the TinyJaguar boasts four, yes that’s four MSI AM1I Mini-ITX motherboards, each with 4GB of DDR memory.

A Raspberry Pi with custom software manages the cluster, and along with some TTL and relays, controls the power to the four nodes. The mini super computer resides in a custom acrylic case held together by an array of 3D printed parts and fasteners.There’s even a rack-like faceplate near the bottom to host the RPi, an Ethernet switch, an array of status LEDs, and the two buttons.

With 16 total cores of computing power (including GPU), the TinyJaguar is quite capable of doing some pretty cool stuff such as running Jupyter notebook with IPyParallel. [Colin] ran into some issues getting the GPU to behave with PyOpenCL. It took a bit of pain and time, but in the end he was able to get the GPUs up, and wrote a small message passing program to show two of the cores were up and working together.

Be sure to check out [Colin’s] super computer project page, specifically the ten project logs that walk through everything that went into this build. He also posted his code if you want to take a look under the hood.

Raspberry Pi Zero Cluster Packs a Punch

If you could actually buy 16 Raspberry Pi Zeros, you might be able to build your very own Raspberry Pi Cluster for only $80! Well… minus the cost of the board to tie them all together…

A Japanese company called Idein is developing a Raspberry Pi module called the Actbulb for computational sensing and data analysis. In order to perform internal testing they decided to make things easier for themselves by developing a board to allow them to plug in not one, not two, but sixteen Raspberry Pi Zeros:

Since we will use Pi’s GPU for image processing, deep learning, etc. We need real Pis but not just Linux machines. Another reason. It can be used for flashing eMMCs of our devices via USB ports when we have to do that by ourselves.

Continue reading “Raspberry Pi Zero Cluster Packs a Punch”

AppleCrate II doubles the cluster computing fun


Back in 2004, Apple hobbyist/guru [Michael Mahon] built a cluster of Apple IIe main boards dubbed the “AppleCrate” as an experiment in parallel computing. Now that a few years have passed, he is back with a new iteration of the device, aptly named AppleCrate II.

AppleCrate II was built to address some of the design limits of his first cluster project as well as to expand his parallel computing capabilities. His gripes with the first model were primarily structural in nature. The new system is organized in horizontal layers, using metal standoffs between each main board, rather than relying on a shaky wooden superstructure to keep things together. He also found his previous 8-processor configuration a bit limiting, so the AppleCrate II has 17 nodes – 16 slaves and one main board dedicated to running the operation. The cluster even uses his own homebrew networking stack known as NadaNet to enable communications between the boards.

The project is pretty impressive, so be sure to swing by his site if you want to learn more.  He has a ton of technical details there, as well as copies of all of the software he used to get the cluster up and running.

[via BoingBoing]

BeagleBoard Cluster

What do you do after you make a BeagleBoard graphing calculator? [Matt] over at Liquidware Antipasto made a BeagleBoard Elastic R Cluster that fits in a briefcase. Ten BeagleBoards, are connected to each other though USB to ethernet adapters and a pair of ethernet switches connected to a wireless router. The cost for this cluster comes in around $2000 and while consuming less than 40 watts of power, out-paces a $4500 laptop. How might you use this cluster? What improvements would you make? Continue reading “BeagleBoard Cluster”

Distributed computing in JavaScript


We’ve heard about the idea of using browsers as distributed computing nodes for a couple years now. It’s only recently, with the race towards faster JavaScript engines in browsers like Chrome that this idea seems useful. [Antimatter15] did a proof of concept JavaScript implementation for reversing hashes. Plura Processing uses a Java applet to do distributed processing. Today, [Ilya Grigorik] posted an example using MapReduce in JavaScript. Google’s MapReduce is designed to support large dataset processing across computing clusters. It’s well suited for situations where computing nodes could go offline randomly (i.e. a browser navigates away from your site). He included a JavaScript snippet and a job server in Ruby. It will be interesting to see if someone comes up with a good use for this; you still need to convince people to keep your page open in the browser though. We’re just saying: try to act surprised when you realize Hack a Day is inexplicably making your processor spike…

[via Slashdot]

Render your next render farm

You might remember [Janne]’s IKEA cluster. Now he’s got a couple of dream rigs in mind, so he started doing 3D renderings of them. Helmer 2 is designed to contain 24 video cards attached to six motherboards with quad core CPUs. (AMD has even taken enough interest to send him some cpus to get started) The rendering really comes in handy for designing the custom copper heat pipes and the aluminum cooling fin enclosure. Still bored, he put together a rendering of a 4 PetaFLOP machine using 2160 video cards.
Update: The Helmer 2 link is fixed.