NVIDIA kicked off their line of GPU-accelerated single board computers back in 2014 with the Jetson TK1, a $200 USD development system for those looking to get involved with the burgeoning world of so-called “edge computing”. It was designed to put high performance computing in a small and energy efficient enough package that it could be integrated directly into products, rather than connecting to a data center half-way across the world.
The TK1 was an impressive piece of hardware, but not something the hacker and maker community was necessarily interested in. For one thing, it was fairly expensive. But perhaps more importantly, it was clearly geared more towards industry types than consumers. We did see the occasional project using the TK1 and the subsequent TX1 and TX2 boards, but they were few and far between.
Then came the Jetson Nano. Its 128 core Maxwell CPU still packed plenty of power and was fully compatible with NVIDIA’s CUDA architecture, but its smaller size and $99 price tag made it far more attractive for hobbyists. According to the company’s own figures, the number of active Jetson developers has more than tripled since the Nano’s introduction in March of 2019. With the platform accessible to a larger and more diverse group of users, new and innovative applications for machine learning started pouring in.
Cutting the price of the entry level Jetson hardware in half was clearly a step in the right direction, but NVIDIA wanted to bring even more developers into the fray. So why not see if lightning can strike twice? Today they’ve officially announced that the new Jetson Nano 2GB will go on sale later this month for just $59. Let’s take a close look at this new iteration of the Nano to see what’s changed (and what hasn’t) from last year’s model.
Continue reading “NVIDIA Announces $59 Jetson Nano 2GB, A Single Board Computer With Makers In Mind”
The failed launch of Soyuz MS-10 on October 11th, 2018 was a notable event for a number of reasons: it was the first serious incident on a manned Soyuz rocket in 35 years, it was the first time that particular high-altitude abort had ever been attempted, and most importantly it ended with the rescue of both crew members. To say it was a historic event is something of an understatement. As a counterpoint to the Challenger disaster it will be looked back on for decades as proof that robust launch abort systems and rigorous training for all contingencies can save lives.
But even though the loss of MS-10 went as well as possibly could be expected, there’s still far reaching consequences for a missed flight to the International Space Station. The coming and going of visiting vehicles to the Station is a carefully orchestrated ballet, designed to fully utilize the up and down mass that each flight offers. Not only did the failure of MS-10 deprive the Station of two crew members and the experiments and supplies they were bringing with them, but also of a return trip which was to have brought various materials and hardware back to Earth.
But there’s been at least one positive side effect of the return cargo schedule being pushed back. The “Spaceborne Computer”, developed by Hewlett Packard Enterprise (HPE) and NASA to test high-performance computing hardware in space, is getting an unexpected extension to its time on the Station. Launched in 2017, the diminutive 32 core supercomputer was only meant to perform self-tests and be brought back down for a full examination. But now that its ticket back home has been delayed for the foreseeable future, NASA is opening up the machine for other researchers to utilize, proving there’s no such thing as a free ride on the International Space Station.
Continue reading “The Space Station Has A Supercomputer Stowaway”
The U.S. Department of Energy’s National Nuclear Security Administration (NNSA) and its three national labs this week announced they have reached an agreement for an open-source Fortran front-end for Higher Performance Computing (HPC). The agreement is with IBM? Microsoft? Google? Nope, the agreement is with NVIDIA, a company known for making graphics cards for gamers.
The heart of a graphics card is the graphics processor unit (GPU) which is an extremely powerful computing engine. It’s actually got more raw horsepower than the computer CPU, although not as much as many claim. A number of years ago NVIDIA branched into providing compiler toolsets for their GPUs. The obvious goal is to drive sales. NVIDIA will use as a starting point their existing Fortran compiler and integrate it with the existing LLVM compiler infrastructure. That Fortran, it just keeps chugging along.
You can try out GPU programming on your Raspberry Pi. Yup! Even it has one, a Broadcom. Just follow the directions from Raspberry Pi Playground. You’re going to get your hands dirty with assembly language so this is not for the faint hearted. One of the big challenges with GPUs is exchanging data with them which gets into DMA processing. You could also take a look at [Pete Warden’s] work on using the Pi’s GPU.
Still wondering about the performance of CPU vs GPU? Here’s Adam Savage taking a look…
Continue reading “DOE Announces A High Performance Computing Fortran Compiler Agreement”