We’re not in the habit of recommending Kickstarter projects here at Hackaday, but when prototype hardware shows up on our desk, we just can’t help but play with it and write it up for the readers. And that is exactly where we find ourselves with the Turing Pi 2. You may be familiar with the original Turing Pi, the carrier board that runs seven Raspberry Pi Compute boards at once. That one supports the Compute versions 1 and 3, but a new design was clearly needed for the Compute Module 4. Not content with just supporting the CM4, the developers at Turing Machines have designed a 4-slot carrier board based on the NVIDIA Jetson pinout. The entire line of Jetson devices are supported, and a simple adapter makes the CM4 work. There’s even a brand new module planned around the RK3588, which should be quite impressive.
One of the design decisions of the TP2 is to use the mini-ITX form-factor and 24-pin ATX power connection, giving us the option to install the TP2 in a small computer case. There’s even a custom rack-mountable case being planned by the folks over at My Electronics. So if you want 4 or 8 Raspberry Pis in a rack mount, this one’s for you.
Continue reading “Turing Pi 2: The Low Power Cluster”
You’ve probably heard it said that clustering a bunch of Raspberry Pis up to make a “supercomputer” doesn’t make much sense, as even a middle-of-the-road desktop could blow it away in terms of performance. While that may be true, the reason most people make Pi clusters isn’t for raw power, it’s so they can build experience with parallel computing without breaking the bank.
So while there was probably a “better” way to produce the Mandelbrot video seen below, creator [Michael Kohn] still learned a lot about putting together a robust parallel processing environment using industry standard tools like Kubernetes and Docker. Luckily for us, he was kind enough to document the whole process for anyone else who might be interested in following in his footsteps. Whatever your parallel task is, and whatever platform it happens to be running on, some of the notes here are likely to help you get it going.
It’s not the biggest Raspberry Pi cluster we’ve ever seen, but the four Pi 4s and the RGB LED festooned enclosure they live in make for an affordable and space-saving cluster to hone your skills on. Whether you’re practicing for the future of software development and deployment, or just looking for something new to play around with, building one of these small-scale clusters is a great way to get in on the action.
Continue reading “Learning The Ropes With A Raspberry Pi Mandelbrot Cluster”
The Pine64 folks have given us so many tasty pieces of hardware over the last few years, but it’s fair to say that their products are for experimenters rather than consumers and can thus be a little rough around the edges at times. Their Clusterboard for example is a Mini-ITX PCB which takes up to seven of their SOPINE A64 compute modules, and networks them for use as a cluster by means of an onboard Gigabit Ethernet switch. It’s a veritable powerhouse, but it has an annoying bug in that it appears reluctant to restart when told. [Eric Draken] embarked upon a quest to fix this problem, and while he got there in the end his progress makes for a long and engrossing read.
We journey through the guts of the board and along the way discover a lot about how reset signals are generated. The eventual culprit is a back-EMF generated through the reset distribution logic itself causing the low-pulled line to never quite descend into logic 0 territory once it has been pulled high, and the solution an extremely simple application of a diode. For anyone who wishes to learn about logic level detective work it’s well worth a look. Meanwhile the board itself with its 28 ARM cores appears to have plenty of potential. It’s even a board we’ve mentioned before, in a personal supercomputer project.
Cluster computing is a popular choice for heavy duty computing applications. At the base level, there are hobby clusters often built with Raspberry Pis, while the industrial level involves data centers crammed with servers running at full tilt. [greg] wanted something cheap, but with x86 support – so set about building a rig his own way.
The ingenious part of [greg]’s build comes in the source computers. He identified that replacement laptop motherboards were a great source of computing power on the cheap, with a board packing an i7 CPU with 16GB of RAM available from eBay for around £100, and with i5 models being even cheaper. With four laptop motherboards on hand, he set about stacking them in a case, powering them, and hooking them up with the bare minimum required to get them working. With everything wrapped up in an old server case with some 3D printed parts to hold it all together, he was able to get a 4-node Kubernetes cluster up and running for an absolute bargain price.
We haven’t seen spare laptop motherboards used in such a way before, but we could definitely see this becoming more of a thing going forward. The possibilities of a crate full of deprecated motherboards are enticing for those building clusters on the cheap. Of course, more nodes is more better, so check out this 120 Pi cluster to satiate your thirst for raw FLOPs.
Classic motorcycles are the wild west of information displays. Often lacking even basic instrumentation such as a fuel gauge and sometimes even a speedometer, motorcycles have come a long way in instrument cluster design from even 20 years ago. There’s still some room for improvement, though, and luckily a lot of modern bikes have an ECU module that can be tapped into for some extra information as [mickwheelz] illustrates with his auxiliary motorcycle dashboard.
This display is built for a modern Honda enduro, and is based upon an ESP32 module. The ESP32 is tied directly into the ECU via a diagnostic socket, unlike other similar builds that interface with a CAN bus specifically. It can monitor all of the bike’s activity including engine temperature, throttle position, intake air temperature, and whether or not the bike is in neutral. [mickwheelz] also added an external GPS sensor so the new display can also show him GPS speed and location information within the same unit.
[mickwheelz] credits a few others for making headway into the Honda ECU. [Gonzo] created a similar build using a Raspberry Pi and more rudimentary screen but was instrumental in gathering the information for this build. If you’re looking for a display of any kind for your antique motorcycle which is lacking an ECU, though, we would suggest a speedometer made with nixie tubes.
It isn’t that hard to assemble an array of Raspberry Pi boards and there are several reasons you might want to do so. The real trick is getting power to all of them and cooling all of them without having a mess of wires and keeping them all separated. The ClusterCTRL stack lets you stack up to five Raspberry Pi boards together. The PCB aligns vertically along the side of the stack of Pis with sockets for each pin header. Using a single 12 to 24V supply, it provides power for each board, a USB power connection, and provisions for two fans. There is also a USB port to control the fans and power.
There’s also a software component to deliver more granular control. Without using the software, the PI’s power on in one second and monitor a GPIO pin to control the fans. With the software, you can turn on or off individual nodes, gang the two fans to turn on together, and even add more stacks.
There is a case that you can print from STL files, although you can buy them preprinted on the Tindie listing where the bulk of information on ClusterCTRL is found. You could also have a 3D printing vendor run off a copy for you if you’d rather.
The power supply is a 10A 5.1V DC to DC converter. That works out to 2A per Pi and 51W total. The power supply for the input, then, needs to be enough to cover 51W, the power for the fans, and some overhead for regulator inefficiency and other small overhead.
We’ve seen a lot of Pi clusters over the years including one that is a good learning tool for cluster management. Of course, there’s always the Oracle cluster with 1,060 boards, which is going to take a bigger power supply.
If you’ve programmed much in Linux or Unix, you’ve probably run into the fork system call. A call to fork causes your existing process — everything about it — to suddenly split into two complete copies. But they run on the same CPU. [Tristan Hume] had an idea. He wanted to have a call, telefork, that would create the copy on a different machine in a Linux cluster. He couldn’t let the idea go, so he finally wrote the code to do it himself.
If you think about it, parts of the problem are easy while others are very difficult. For example, creating a copy of the process’s code and data isn’t that hard. Since the target is a cluster, the machines are mostly the same — it’s not as though you are trying to move a Linux process to a Windows machine.
Continue reading “Beam Your Program To Another Computer”