It’s that time of year again, with the holidays fast approaching friends and family will be hounding you about what trinkets and shiny baubles they can pretend to surprise you with. Unfortunately there’s no person harder to shop for than the maker or hacker: if we want it, we’ve probably already built the thing. Or at least gotten it out of somebody else’s trash.
But if they absolutely, positively, simply have to buy you something that’s commercially made, then you could do worse than pointing them to this very slick Raspberry Pi cluster backplane from [miniNodes]. With the ability to support up to five of the often overlooked Pi Compute Modules, this little device will let you bring a punchy little ARM cluster online without having to build something from scratch.
The Compute Module is perfectly suited for clustering applications like this due to its much smaller size compared to the full-size Raspberry Pi, but we don’t see it get used that often because it needs to be jacked into an appropriate SODIMM connector. This makes it effectively useless for prototyping and quickly thrown together hacks (I.E. everything most people use the Pi for), and really only suitable for finished products and industrial applications. It’s really the line in the sand between playing around with the Pi and putting it to real work.
[miniNodes] calls their handy little device the Carrier Board, and beyond the obvious five SODIMM slots for the Pis to live in, there’s also an integrated gigabit switch with an uplink port to get them all connected to the network. The board powers all of the nodes through a single barrel connector on the side opposite the Ethernet jack, leaving behind the masses of spider’s web of USB cables we usually see with Pi clusters.
The board doesn’t come cheap at $259 USD, plus the five Pi Compute Modules which will set you back another $150. But for the ticket price you’ll have a 20 core ARM cluster with 5 GB of RAM and 20 GB of flash storage in a 200 x 100 millimeter (8 x 4 inch) footprint, with an energy consumption of under 20 watts when running at wide open throttle. This could be an excellent choice for mobile applications, or if you just want to experiment with parallel processing on a desktop-sized device.
Amazon is ready for the coming ARM server revolution, are you? Between products like this and the many DIY ARM clusters we’ve seen over the years, it looks like we’re going to be dragging the plucky architecture kicking and screaming into the world of high performance computing.
[Thanks to Baldpower for the tip.]
There seems to be a universal truth on the Internet: if you open up a service to the world, eventually somebody will come in and try to mess it up. If you have a comment section, trolls will come in and fill it with pedantic complaints (so we’ve heard anyway, naturally we have no experience with such matters). If you have a service where people can upload files, then it’s a guarantee that something unsavory is eventually going to take up residence on your server.
Unfortunately, that’s exactly what [Christian Haschek] found while developing his open source image hosting platform, PictShare. He was alerted to some unsavory pictures on PictShare, and after he dealt with them he realized these could be the proverbial tip of the iceberg. But there were far too many pictures on the system to check manually. He decided to build a system that could search for NSFW images using a trained neural network.
The nude-sniffing cluster is made up of a trio of Raspberry Pi computers, each with its own Movidius neural compute stick to perform the heavy lifting. [Christian] explains how he installed the compute stick SDK and Yahoo’s open source learning module for identifying questionable images, the aptly named open_nsfw. The system can be scaled up by adding more Pis to the system, and since it’s all ARM processors and compute sticks, it’s energy efficient enough the whole system can run off a 10 watt solar panel.
After opening up the system with a public web interface where users can scan their own images, he offered his system’s services to a large image hosting provider to see what it would find. Shockingly, the system was able to find over 3,000 images that contained suspected child pornography. The appropriate authorities were notified, and [Christian] encourages anyone else looking to search their servers for this kind of content to drop him a line. Truly hacking for good.
This isn’t the first time we’ve seen Intel’s Movidius compute stick in the wild., and of course we’ve seen our fair share of Raspberry Pi clusters. From 750 node monsters down to builds which are far more show than go.
We’ve seen the supercomputer cluster work of [Nick Smith] from the UK before, but his latest build is quite lovely. This time around, he put together a 96-core supercomputer using the NanoPi Fire3, a Raspberry Pi alternative that has double the number of cores. His post takes you through how he built the supercomputer cluster, from designing the laser-cut acrylic case to routing the power cables.
Continue reading “NanoPi Cluster Is Quiet, Cool And Has Blinky Lights”
When the ESP32 microcontroller first appeared on the market it’s a fair certainty that somewhere in a long-forgotten corner of the Internet a person said: “Imagine a Beowulf cluster of those things!”.
Someone had to do it, and it seems that the someone in question was [Kodera2t], who has made a mini-cluster of 4 ESP32 modules on a custom PCB. They might not be the boxed computers that would come to mind from a traditional cluster, but an ESP32 module is a little standalone computer with processing power that wouldn’t have looked too bad on your desktop only in the last decade. The WiFi on an ESP32 would impose an unacceptable overhead for communication between processors, and ESP32s are not blessed with wired Ethernet, so instead the board has a parallel bus formed by linking together a group of GPIO lines. There is also a shared SPI SRAM chip with a bus switchable between the four units by one of the ESp32s acting as the controller.
You might ask what the point is of such an exercise, and indeed as it is made clear, there is no point beyond interest and edification. It’s unclear what software will run upon this mini-cluster as it has so far only just reached the point of a first hardware implementation, but since ESP32 clusters aren’t exactly mainstream it will have to be something written especially for the platform.
This cluster may be somewhat unusual, but in the past we’ve brought you more conventional Beowulf clusters such as this one using the ever-popular Raspberry Pi.
When we first saw [Ajlitt’s] Hackaday.io project Terrible Cluster we thought, perhaps, he meant terrible in the sense of the third definition:
3. exciting terror, awe, or great fear; dreadful; awful. (Dictionary.com)
After looking at the subtitle, though, we realized he just meant terrible. The subtitle, by the way, is: 5 Raspberry PI Zeros. One custom USB hub. Endless disappointment.
There are four Raspberry Pi Zero boards that actually compute and one Raspberry Pi Zero W serves as a head node and network router. The total cost is about $100 and half of that is in SD cards. There’s a custom USB backplane and even a 3D-printed case.
At first, using five tiny computers in a cluster might not seem like a big deal. Benchmarking shows the cluster (with a little coaxing) could reach 1.281 GFLOPS, with an average draw of 4.962W. That isn’t going to win any world records. However, the educational possibilities of building a $100 cluster that fits in the palm of your hand is interesting. Besides, it is simply a cute build.
We’ve seen much larger Pi clusters, of course. You might be better off with some desktop CPUs, but — honestly — not much better.
[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.
One way to get around limitations in computing resources is to throw more computers at the problem. That’s why even cheap consumer-grade computers and phones have multiple cores in them. In supercomputing, it is common to have lots of processors with sophisticated sharing mechanisms.
[Henk Verbeek] decided to take 80 inexpensive PIC32 chips and build his own cluster programmed in — of all things — BASIC. The devices talk to each other via I2C. His example application plots fractals on another PIC32-based computer that has a VGA output. You can see a video of the device in action, below.
Continue reading “80-PIC32 Cluster Does Fractals”