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Adapteva Parallella Supercomputing Boards Start Shipping
hypnosec writes "Adapteva has started shipping its $99 Parallella parallel processing single-board supercomputer to initial Kickstarter backers. Parallella is powered by Adapteva's 16-core and 64-core Epiphany multicore processors that are meant for parallel computing unlike other commercial off-the-shelf (COTS) devices like Raspberry Pi that don't support parallel computing natively. The first model to be shipped has the following specifications: a Zynq-7020 dual-core ARM A9 CPU complemented with Epiphany Multicore Accelerator (16 or 64 cores), 1GB RAM, MicroSD Card, two USB 2.0 ports, optional four expansion connectors, Ethernet, and an HDMI port."
They are also releasing documentation, examples, and an SDK (brief overview, it's Free Software too). And the device runs GNU/Linux for the non-parallel parts (Ubuntu is the suggested distribution).
Very well:
Imagine a Beuowulf Cluster of these!
I could buy enough of these to cover the underside of the floor of my house and mine Bitcoins during the winter. Then I get radiant heat and useless fake money (which is probably just NSA's password cracker anyways).
sudo make me a sandwich
Anyone out there in /.-land plan on getting these for a real project?
Tell us about it! What language/OS/purpose?
Just curious...
The ARM cores serve as a host for the Epiphany cores, roughly similar to the way an X86 CPU serves as a host to your video card. Epiphany is not ARM, it is a chip with a number of 1 GHz RISC cores that all communicate via a network-on-chip. So, it is optimized for doing a lot of floating-point arithmetic at very low power consumption.
Help me out here. The Adapteva sales pitch is claiming you get faster time to market by not having to do any FPGA programming (ANSI-C and OpenCL for the multicore coprocessors). The Zynq processor seems to be just for the host OS, which they say can run Ubuntu out of the box and they provide open source development tools for everything else. No mention of Xilinx anywhere that I can see. Am I missing something?
he was probably confusing this with http://www.kickstarter.com/projects/1106670630/mojo-digital-design-for-the-hobbyist
which pretty much means he didn't read even half of TFS.
world was created 5 seconds before this post as it is.
I'm skeptical as to how useful this chip will be. High core counts are making supercomputing more and more difficult. Supercomputing isn't about getting massively parallel, but rather high compute performance, memory performance, and interconnect performance. If you can get the same performance out of fewer cores, then there will usually be less stress on interconnects. Parallel computing is a way to get around the limitations on building insanely fast non-parallel computers, not something that's particularly ideal. For things like graphics that are easily parallel, it's not much of a problem, but collective operations on supercomputers with hundreds of thousands to millions of cores are one of the largest bottlenecks in HPC code.
Supercomputers are usually just measured by their floating point performance, but that's not really what makes a supercomputer a supercomputer. You can get a cluster of computers with high end graphics cards, but that doesn't make it a supercomputer. Such clusters have a more limited scope than supercomputers due to limited interconnect bandwidth. There was even debate as to how useful GPUs would really be in supercomputers due to memory bandwidth being the most common bottleneck. Supercomputers tend to have things like Infiniband networking in multidimensional torus configurations. These fast interconnects give the ability to efficiently work on problems that depend on neighboring regions, and are even then a leading bottleneck. When you get to millions of processors, even things like FFT that have, in the past, been sufficiently parallel, start becoming problems.
Things like Parallella could be decent learning tools, but having tons of really weak cores isn't really desirable for most applications.
Looking at the FPGA code, it targets Xilinx devices. The OP points out using proprietary (but free) tools from Xilinx makes it "not open", I guess in the same way that the chips used on the card are "not open". I think it misses the point, but whatever.
It has about half the gigaflops of a Core i7, and costs 80% less to buy.
It uses 5-10 watts, whereas the Core i7 uses 100 - 200 watts, with the chipset.
So total cost of ownership is about 90% less than the Core i7. Ten of them would spank the heck out of a Core i7 and cost the same.
> and what can you run on it ?
16 or 64 cores is good for facial recognition, audio processing, video processing, some network stuff - things where you run the same function on many pixels / samples / rows. So for face recognition, for example, the image would be broken up into 64 blocks and all of the blocks analyzed simultaneously on the 64 cores.
A database designed for the many cores could work well. For example, say you need to sort a table with 100,000 rows. On a system like this with 64 cores,
each core could simultaneously sort a group of 1,500 rows, then you'd merge those 64 sorted groups together ala merge sort. As a firewall, it could handle a blacklist with a million entries, as each core would handle simultaneously apply 1/64 of that list.
With 64 cores, I'd say it's already a cluster. A dozen of these ($1200) would have 768 cores and fit in a microatx case. :)
Yeah but compare it to a GPGPU and you start to realize how slow it is, a $200 660 GTX does 1880 GFLOPS in 140W.
1 GFLOPS/$ versus 9.4 GFLOPS/$
10 GFLOPS/Watt versus 13.4 GFLOPS/Watt
There are 4 boxes to use in the defense of liberty: soap, ballot, jury, ammo. Use in that order. Starting now.
It is a shame that you posted as an anonymous coward here. I'ld love to understand your thinking on this. As far as I see it, this is a win as the source code for the FPGA logic will be open, making this much like using Visual Studio to build an other Open Source project - hardly an Open Source fail.
I would also like to know if you run on Sparc CPUs as they are "open" (with published HDL source), rather than on Intel or ARM? If not, how can you defend that your favourite Open Source project (say Apache) running on Linux on an Intel system board is more "Open Source" than this? Do you have the source code for your MoBo's chipset?
You will with the Parallella...
16 or 64 cores is good for facial recognition, audio processing, video processing, some network stuff
Low end ARM cores do that already in a low cost, low power package. I really can't see how this device would be economic for any of those things - even if you need to do facial recognition on multiple image streams at once low cost ARM cores will be cheaper. You also have the difficulty of interfacing so many video streams to a single parallel processing device; it would be easier to have lots of smaller devices.
As a firewall, it could handle a blacklist with a million entries
Again, current ARM based routers can handle such lists. IP address lists or simple URL lists with a few wildcards are no problem. I suppose if you wanted a million complex regex rules then having 64 cores would help, but if you do have such a list you need to write better regular expressions.
Low end servers are about the only application where this makes sense, and even then the added cost of having to write software specifically for these cores probably outweighs any power/performance gains over ARM and you still have the I/O issues I mentioned earlier.
const int one = 65536; (Silvermoon, Texture.cs)
SJW, n: "Someone I don't like, and by the way I'm a fuckwit" - AC
the FPGA is the host for the CPU and communications with the Epiphany processor so you never need to change the FPGA at all. it's the Epiphany processor is what you are developing for, not an FPGA. the functionality of the FPGA is open, so you could use it just like any other IC if you really wanted.
Anons need not reply. Questions end with a question mark.
Proprietary software which can be used for free with very reasonable size and device limitations. Plus if you don't like the GUI you can always run the traditional command line tools to build a bitstream if you want.
I am becoming gerund, destroyer of verbs.
The only problem is you cant run GPU standalone.
There was one project by someone who reverse engineered old Radeon HD2400
http://www.edaboard.com/thread236934.html
http://www.flickr.com/photos/73923873@N05/sets/72157631771354007/
but that guy deleted his git repo before publishing the news blurp and some photos and they quickly shut up about it.
I would love to be able to use GPU cards standalone for Vision projects, or just as a openCL accelerators for embedded systems.
Who logs in to gdm? Not I, said the duck.
now mount that HD7870 inside RC plane, or a quad drone
the closest you can get is mali t604 doing 68 GFLOPS or mali t658 at 272 GFLOPS (theoretical numbers, but everyone including amd uses those)
Who logs in to gdm? Not I, said the duck.
manufacturing of physical goods can still be paid
How magnanimous of you.
In other words: You deal with organized crime.
By your standards, 100% of the electronics, computer and software industry is organized crime. That may stroke your ideological fervor, but it's of little practical value. Even Linus Torvalds uses a machine where less than 100% of the IP for all parts, software and manufacturing equipment is open. I'll happily continue using devices, participating in that industry and earning a living. I'm wondering how you made that post while avoiding any contact with the product of, as you label it, organized crime.
bingo. if you've seen some of the crazy acrobatic stuff being done with quad copters over on TED that is using several remote PCs and remote control. The programming could probably all be packed into one of these boards and built right into each copter.
I don't know how these parallella boards work, but hopefully they would be a bit more versatile.
There is almost no chance that a $100 board can be designed to have a memory interface that can keep 64 cores well fed at this point in time. They have almost certainly chosen low latency cache model over high bandwidth cache model due to this, so this product will probably only perform well on highly computational problems that dont require much memory - in other words none of the problems that GPU's struggle with will likely be any better on it.
"His name was James Damore."