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.
