ask what future things having something like this on a SoC would enable.
I asked that and came up blank. And I haven't seen answers from anyone else, either. Has Adapteva themselves shown any examples where their chip beats a GPU?
I can think of two: documentation and simplicity.
Comparing Parallella with GPUs only in performance is missing the point. The board is open and quite understandable for non-experts like me. It's a platform for learning and experimenting, like a Raspberry Pi but more geeky. You will probably have an easier time tinkering with this and getting it to do useful things, that's the point.
I feel, like the parent commenter, quite excited about this.
In the comment thread on the article someone points out that the Adapteva chip doesn't do double precision floating-point, which limits its usefulness (to put it mildly). If the goal is to provide people with a low-cost platform to experiment with parallel programming, surely a decent NVidia card gives you less expensive (given you can plug it into a PCI slot and it will work) access to more CPUs that run faster and do more.
It took a long time for GPU's to get double precision floating point and plenty of GPGPU work was done with them prior to that, so it's not a deal breaker
Not sure if world first or AMD's first, but it was around this timeframe, 2007:
"AMD Delivers First Stream Processor with Double Precision Floating Point Technology"
http://phys.org/news113757140.html
I'm 32 or so years of programming, I've hardly ever done anything that needed, or used, floats. It may limit it's usefulness, but most of what people tend to want double precision for is incidentally also stuff that is easily vectorized, in which case a GPU will crush it anyway.
And a "decent NVidia card" doesn't allow me to combine arbitrary independent C programs to each individual core, and doesn't give me full low level guides for hardware access. It's a completely different beast.
Well, you can still do double-floats, combining two 32-bit floats for a greater precision. While that doesn't get you full double precision, it just might be enough. And of course you can extend the same idea to implement quad-floats and so on.
One interesting application could be realtime 3D rendering because this is an area with small overhead. I know that the chip does not support floating point but that could be simulated by fixed point integers.
I agree with Shamanmuni that the great advantage of Parallela chip over GPUs is open source (full documentation). It's a practical study tool for real parallel programming tasks that many students can afford.
"I fundamentally disagree that SoCs are different than desktop technology. They're just smaller. High-end architectures are already driven to be as power-efficient as possible, so when you cut them down they're still efficient. For example, Kabini is an "SoC" that has the same GCN CU as a "desktop" Kaveri or a discrete Radeon."
The Parallella doesn't seem inherently more appropriate for embedded devices; it just depends on your requirements. Kabini would be embarrassingly power-hungry in plenty of embedded applications, while the Parallella might be laughably slow in plenty of other embedded applications.
Don't forget, by the way, that "embedded" doesn't mean "battery".
You're nitpicking. I already gave a few examples of where Parallella would be a good fit. To remind you, let's revisit the OpenCV for robotics application. The Parallella is shaping up to be a great device for OpenCV applications, do you at least admit that?
I see, I am less confused now. You commented elsewhere on this page, and assume that we all have read those comments, though they are not in this chain right here.
You're approaching the question from a different angle. The key word is SoC. Think embedded performance, rather than desktop performance.
Just to give you a few examples... OpenCV for robotics platforms, cheap low-power SDR capable of transmission, SIP encryption and compression. One might argue you could stick a GPU in a robot, I'd personally want something better suited to the task (lower power).
I fundamentally disagree that SoCs are different than desktop technology. They're just smaller. High-end architectures are already driven to be as power-efficient as possible, so when you cut them down they're still efficient. For example, Kabini is an "SoC" that has the same GCN CU as a "desktop" Kaveri or a discrete Radeon.
This is wrong. For example, low-power embedded ARM chips are not simply cut-down high-end x86 chips. If you optimize for power usage instead of raw performance, there are many design decisions that come out differently, resulting in a design that is qualitatively different and not just "scaled down powerhorse".
ZenoArrow is talking about embedded SoCs though. While Kabini is a SoC, it isn't really suited for embedded applications. These chips usually have a ton of GPIO, built-in support for different communication protocols, analog to digital converters, a lower power draw, etc. etc.
I asked that and came up blank. And I haven't seen answers from anyone else, either. Has Adapteva themselves shown any examples where their chip beats a GPU?