Parallella: A Supercomputer For Everyone

I picked up a raspberry Pi a few days ago. Initially, I was blown away by the low price point. Since then, I've been reflecting on what makes a computer useful.<p>For personal computers - desktops and laptops - I think we don't have a shortage of processor cycles. The minimal specs of the Raspberry Pi make it useable - 256MB of RAM, 700 MHz CPU, a few GB of storage and enough MB to saturate a home broadband connection. What is compelling about the best contemporary personal computing devices is form factor. How easy is it to provide input; how nice is the screen; if it is a mobile device, how heavy is it and does the battery last long enough, etc.<p>Does a personal parallel computer really help me? At first blush, I am having a hard time seeing how. Clearly, there are CPU intensive workloads that people have mentioned in this discussion - ray tracing is one. The video mentions robotics and algorithms. I have mixed feelings about that since I personally believe the future of robotics lies in computation off the physical robot itself - aka cloud robotics. A use case I personally would find beneficial is the ability to run dozens of VMs on the same machine. Heck ... each of my 50 open browser tabs could run inside separate VMs. I know light weight container technology is around for a while. e.g. jails, LXC. But what about hypervisor-based virtualization - e.g. VMWare, Xen, etc.? While the parallelization offered by this tech would be awesome, what seems to be missing is the ability to address lots and lots of memory.

This is an interesting project that deserves to reach it's funding goal but progress towards that is slow (I have been keeping an eye on it since launched on Kickstarter).<p>I suspect the problem is that it has no compelling (and immediate) "use case". If they could communicate a set of application ideas then I suspect that a whole new raft of supporters will be happy to risk at least $99.

I have backed this project. This is an interesting startup, with some good solid technology behind it. They have managed to design and tape out a chip with just a 2m dollar budget so far. The main draw of their architecture is not its peak, but rather its efficiency, both in terms of perf/watt and perf/die area. You can look at their manuals on the site.<p>Hoping their funding drive succeeds. I am liking the fact that ISA is being fully documented and we will have a fully open-source toolchain to work with the system.<p>(Disclaimer: Not associated with Adapteva in any way).

I don't really have any comment on the project itself (not something I would ever use and I don't know the value of what they're proposing).<p>But on purely geek terms this thing seems to warrant a "holy shit":<p><a href="http://www.adapteva.com/products/silicon-devices/e64g401/" rel="nofollow">http://www.adapteva.com/products/silicon-devices/e64g401/</a><p>Again I don't know how (un)common that sort of thing is but I wasn't expecting to see 64 cores in that tiny form factor. Does anyone here know how cutting edge this thing is if at all?<p>[Edit]<p>Also does anyone here want to address use cases for this thing?

I think folk need to stop abusing the term "supercomputer".<p>It is not really a performance designation. It doesn't define a certain architecture or design.<p>It is pretty clearly an economic designation.

WTF: "45 GHz of equivalent CPU performance"<p>(though I see the more informative "50 GFLOPS/Watt" below... and I like the prospect of something that would make it cheap to play with large scale real time neural nets...)

I would enjoy making a ray-tracing GPU from one of these.<p>That the cores don't run in lockstep can be shader heaven! I'm imagining using the cores in a pipeline with zoning so some core 'owns' some tile of the screen and does z-buffering, and other core does clipping of graphics primitives for each tile, and a sea of compute nodes between them chew up work and push it onwards.<p>Some kind of using the cores as a spatial index too. Passing rays to other cores as they propagate beyond the aabb belonging to a core.<p>Doubtless it wouldn't work like that. And wouldn't work well. But its fun thinking about it! :)

Parallel computing is limited by Amdalah's Law. Having more core does not mean you can have have more speed because it's not easy to use all those cores. Most imperial languages are not designed with running codes on multiple core and few programers are taught how to design their algorithm for using a handful of cores.<p>I can see this platform being a good tool for students and researchers to experiment with algorithm speedups by making their sequential code, parallel.<p>In my parallel programming class, our teacher had to rig together a computer lab to connect the 12 quad core computers to simulate a 64 core cluster. Then again, 64 core cluster of Parallella would cost like $7000. You can get the same 64 core setup by buying 8 x 8 core consumer desktop computer for under $3000, which will still be more cost effective and probably have ten times more computing power because of the x86 architecture.<p><a href="http://en.wikipedia.org/wiki/Amdahls_law" rel="nofollow">http://en.wikipedia.org/wiki/Amdahls_law</a>

<a href="http://news.ycombinator.com/item?id=4583263" rel="nofollow">http://news.ycombinator.com/item?id=4583263</a>

Without commenting on the merit of this project, I'm alarmed to see a VC-backed making a Kickstarter pitch.

Does anyone know what kind of cores these RISC cores will be? Will it be some lower end ARM version, or MIPS? Will it be something for which a wide array of tooling already exists, or will this have its own custom architecture which only works with their toolchain?

I'd rather see a kickstarter for a book on greenarrays programming :(

What's the point in having such a RAM/core ratio? By assigning 4 threads per core (which is fairly common to exploit manycore architectures) you don't even have 4Meg of memory per thread.<p>I would totally agree that memory constraint is sort of tied to manycore architectures, but in this case I find it pushed to the limits.

Since I thought I saw an Adapteva person posting here earlier:<p>If the Kickstarter falls through, what options could you still make available to hobbyists? Is there some version of your current prototype setup that you could sell, even if it's not one convenient board?

I'm so hoping this gets funded.

we got into a big discussion on super computers (the definition), the meaning of what a core is and a whole bunch of other issues... but the low power requirements of this are being completely ignored... as for applications... well portable and/or remote devices/sensors that need parallel computing capabilities and where high energy usage is prohibitive are possible applications. But the greatest asset of this is to spark the next gen of app developers and programmers to fully embrace parallel programming and truly make software scalable...

Yes but can they mine Bitcoins? They come with OpenCL drivers so in theory they could as most Bitcoin miners have OpenCL interfaces to the GPU.

is it me or erlang would sort of fit nicely into the core's ideology of data processing? Seems that LD is like set constant. there are external STR commands. You can have data loaded into registers from the code - MOV. Not an expert in Erlang but it seem that two ideologies can beneficial to one another.<p>And if it is so, should expecting Erlang compiler be out of the question? :)