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AMD Demonstrates "Teraflop In a Box"
UncleFluffy writes "AMD gave a sneak preview of their upcoming R600 GPU. The demo system was a single PC with two R600 cards running streaming computing tasks at just over 1 Teraflop. Though a prototype, this beats Intel to ubiquitous Teraflop machines by approximately 5 years." Ars has an article exploring why it's hard to program such GPUs for anything other than graphics applications.
It shouldn't be a TERAble FLOP at the stores anyway. Nice performance...
OK, yes, bad pun, bad spelling, you can "-1 get a real sense of humor" me now.
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Look up 'ubiquitous' before you whine about how far behind Intel might seem to be.
Though having one demonstration will help spur the demand, and the demand will spur production, I still think it'll be five years before everybody's grandmother will have a Tf lying around on their checkbook-balancing credenza, and every PHB will have one under their desk warming their feet during long conference calls.
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Step 1: Put your chip in the box.
How much is that in BogoMIPS?
I might be (read: am mostly) retarded but I never thought of using a graphics processor for anything else, but with the super cards around the corner it makes sense that some normal processing jobs could be farmed out to the GPU when its not being occupied with graphics duties. Does anyone know where I can find some extra info on this, or to what extent this is being implemented? My curiosity is piqued!
Isn' the reason this is so interestiong because you cannot have a Intel Core 2 Extreme with 2 x nVidia GTXs in a dual SLI arrangement using CUDA pushing a tflop at this present time?
Maybe soon but I thought it isn't _now_!
It might be hard, but then again, it might be worthwhile. For instance (I'm a ham radio operator) I ran into a sampling shortwave radio receiver the other day. Thing samples from the antenna at 60+ MHz, thereby producing a stream of 14-bit data that can resolve everything happening below 30 MHz, or in other words, the entire shortwave spectrum and longwave and so on basically down to DC.
Now, a radio like this requires that the signal be processed; first you separate it from the rest, then you demodulate it, then you apply things like notch filters (or you can do that prior to demodulation, that's very nice) you build an automatic gain control to handle amplitude swings, provide a way to vary the bandwidth and move the filter skirts (low and high) independently... you might like to produce a "panadapter" display of the spectrum around the signal of interest where the is a graph that lays out signal strengths for a defined distance up and down spectrum... you might want to demodulate more than one signal at once (say, a FAX transmission into a map on the one hand, and a voice transmission of the weather on the other.) And so on - I could really go on for a while.
The thing is, as with all signal processing, the more you try to do with a real-time signal, the more resources you have to dedicate. And this isn't audio, or at least, not at the early stages; a 60+ MHz stream of data requires quite a bit more in terms of how fast you have to do things to it than does an audio stream at, say, 44 KHz.
Bit signal processing typically uses fairly simple math; a lot of it, but you can do a lot without having to resort to real craziness. A teraflop of processing that isn't even happening on the CPU is pretty attractive. You'd have to get the data to it, and I'm thinking that would be pretty resource intensive, but between the main CPU and the GPU you should have enough "ooomph" left over to make a beautiful and functional radio interface.
There is an interesting set of tasks in the signal processing space; forming an image of what is going on under water from sound (not sonar... I'm talking about real imaging) requires lots and lots of signal processing. Be a kick to have it in a relatively standard box, with easily replaceable components. Maybe you could do the same thing above-ground; after all, it's still sound and there are still reflections that can tell you a lot (just observe a bat.)
The cool thing about signal processing is that a lot of it is like graphics, in a way; generally, you set up some horrible sequence of things to do to your data, and then thrash each sample just like you did the last one.
Anyway, it just struck me that no matter how hard it is to program, it could certainly be useful for some of these really resource intensive tasks.
I've fallen off your lawn, and I can't get up.
The chips are a much different ISA, so there's no way that binaries that will run on G80 hardware will run on an R600. Heck, even the ATi R400 series (x700, x8x0) is not binary-compatible with the current R500 x1000 units.Maybe ATi will make a CUDA compiler, but I am guessing that since folks have already gotten going using the R500 hardware (see: http://folding.stanford.edu/ I doubt that AMD/ATi will make a big effort to use a competitor's technology. Please correct me if I am incorrect, but I am not aware of any groups or programs that use NVIDIA hardware as number-crunchers yet.
Just "gittin-r-done," day after day.
Don't mention the wattage...
And the second rule of teraflop club...
Don't mention the wattage...
Back here in the real world where we PAY FOR ELECTRICITY, we're waiting for some nice FLOPS/Watt, keep trying guys.
And they announced this some time ago didn't they?
- Adam L. Beberg - The Cosm Project - http://www.mithral.com/
...generic purposes, it is that they're (GPUs) suited better for certain types of operations. Image processing, as an example, is very well suited to working on a GPU because the GPU excels at addressing and operating on elements of arrays (textures basically.) I've used it as a proof of concept at work for processing large numbers of video feeds simultaneously for things like photometric normalization, image stabilization, et cetera, and the things are awesome. They work well in this scenario because the problem I'm trying to solve fits the caveats of using the GPU well. Slow upload of data, miraculously fast action upon that data, slow download of the data. Now, slow is relative and getting more and more relative as new chipsets are released.
The actual framework for doing this is relatively simple although it certainly did help that I've a background in OpenGL and DirectXGraphics (so I've done shader work before); however, again, progress is removing those caveats as well. Generic GPU programming toolsets are imminent the only problem being ATI has no interest in their toolsets working with nVidia and nVidia has even less interest in their toolset(s) running ATI hardware. Something we'll just have to learn to deal with.
BTW, DirectX10 will make this a little easier as well with changes to how you have to pipeline data in order to operate on it in a particular fashion.
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That should be Teraflops. Flops is Floating-point operations per second, so always has an s on the end even if singular.
So the preview could be boiled down to: Card still in development will be faster than cards currently available for sale.
It also included some pictures of the cooling solution that will completely dominate the card. Not that a picture of a microchip with "R600" written on it would be a lot better I guess. Although the pictures are fuzzy and hard to see, it looks like it might require two separate molex connections just like the 8800s.
I read the internet for the articles.
Imagine a Beowulf cluster of organically connected people imagining Beowulf clusters - I'd have Quake running on you at a squillian FPS in no time!
biopowered.co.uk - catalytically cracking triglycerides for home automotive use since 2008. Just say no to big oil!
which is fully connected to the Internet so that I can put my toast down or pop it up remotely.
Wait...from some of the other comments about electricity usage, I might be able to do away with the heating coils and use the circuits themself to toast. That would really be an environment plus. Wonder how it would affect the taste of the bread?
Help end the use of Sigs. Tomorrow
"Anything other" is "general purpose", which they cover at GPGPU.org. But the general community of global developers hasn't gotten hooked on the cheap performance yet. Maybe if someone got an MP3 encoder working on one of these hot new chips, the more general purpose programmers would be delivering supercomputing to the desktop on these chips.
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make install -not war
You use ambient sound instead of radiating a signal yourself, and you try to resolve the entire environment, rather than just the sound emitting elements in the environment. This makes you a lot harder to detect; it also makes resolving what is going on a lot more difficult. Hence the need for lots of CPU power. In the water or in the air. Passive sonar - at least typically - is intended to resolve (for instance) a ship or a weapon that is emitting noise. But the sea is emitting noise all the time - waves, fish burping, whale calls, shrimp clicking - all kinds of noise, really. Using that noise as the detecting signal is the trick, and it isn't very similar to normal sonar, in terms of what kind of computations or results are required. Classic sonar gives you a range and bearing; this kind of thing is aimed at giving you an actual picture of the environment. It's a lot harder to do, but man, is it cool.
I've fallen off your lawn, and I can't get up.
There's a real difference between getting something to happen on a quasi-DSP like a GPU and on a real, general purpose processor like a CPU. If GPUs were full out CPU replacements, well then we wouldn't have CPUs any more, would we? The problem is that they are very very fast, but only at some things. Now that's fine, because that's what they were designed for. They are made to push pixels really fast and if they can do anything else, well bonus. However it does mean that they aren't a general purpose computing replacement.
Also, the more specialized you get your DSP, the easier it is to get speed out of it. I'm sure it wouldn't be hard to design (I'm sure they already exist) a very narrow purpose DSP that does over 1 trillion floating point ops per second. However that's real different than having a CPU that will do the same, and do it across many kinds of ops.
So as nifty as shit like this might be, it is real disingenuous to pretend that they've "beat" Intel. Intel isn't talking about a graphics card, they are talking about their CPUs. By the numbers my GPU has always been faster than my CPU, as well it should. There'd be no point in paying for specialized hardware if I had general purpose hardware that was faster.
Ars has an article exploring why it's hard to program such GPUs for anything other than graphics applications.
No, Ars has an article blithering that it's hard to program such GPUs for anything other than graphics applications. It doesn't say anything constructive about why.
Here's an reasonably readable tutorial on doing number-crunching in a GPU. The basic concepts are that "Arrays = textures", "Kernels = shaders", and "Computing = drawing". Yes, you do number-crunching by building "textures" and running shaders on them. If your problem can be expressed as parallel multiply-accumulate operations, which covers much classic supercomputer work, there's a good chance it can be done fast on a GPU. There's a broad class of problems that work well on a GPU, but they're generally limited to problems where the outputs from a step have little or no dependency on each other, allowing full parallelism of the computations of a single step. If your problem doesn't map well to that model, don't expect much.
Step 2: Don't leave your box in Boston.
To all the fellas out there with geek friends to impress
It's easy to do, just follow these steps:
One: Cut a hole in a box
Two: Stick your chip in that box
Three: Make her open the box
And that's the way you do it
It's my chip in a box
Well, as I see it, advertizing "[some amazing benchmark] in a box" is reasonably foolish because I could produce a system with amazing theoritical performance that doesn't really perform that much better than a system that is a fraction of the cost ... It wasn't that long ago where you could (easily) buy motherboards that supported 2 or 4 seperate processors, and people have generated Quad-SLi setups; what this means is you could create a 4 processor Core 2 Duo system with a Quad SLi Geforce 8800 GTx which (in most applications) would not perform much better than a single processor Core 2 Duo system with a single Geforce 8800GTx.
The Playstation 3 is reported to harness 2 TFLOPS. But "only" 204GFLOPS run on the Cell CPU, 10%. The other 1.8TFLOPS runs on the nVidia G70 GPU. But the G70 runs shaders, very limited application to anything but actually rendering graphics.
The Cell itself is notoriously hard to code for. If just some extra effort can target the nVidia, that's TWO TeraFLOPS in a $500 box. A huge leap past both AMD and Intel.
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make install -not war
GPGPU is hard because we're still in the very early days of this particular revolution. As I think about it, and from what we know of AMD's plans in particular, I think this is kind of like the evolution of FPU.
See, in the early days FPU was a seperate chip (anyone remember buying an 80387 to plug into their mobo?). Writing code to use FPU was also a complete pain in the ass, because you had to use assembly, with all the memory management and interrupt handling headaches inherent. FPUs from different vendors weren't guaranteed to have completely compatible instruction sets. Because it was such a pain in the ass, only highly special purpose applications made use of FPU code. (And, it's not that computer scientists hadn't thought up appropriate abstractions to make writing floating point easy. Compilers just weren't spitting out FPU code).
Then, things began to improve. The FPU was brought on die, but as an optional component (think 486SX vs 486DX). Languages evolved to support FPUs, hiding all the difficulty under suitible abstractions so programmer could write code that just worked. More applications began to make use of floating point capabilities, but very few required a FPU to work.
Finally, FPU was brought on die as a bog standard part of the CPU. At that point, FPU capabilities could be taken for granted and an explosion of applications requiring an FPU to achieve decent performance ensued (see, for istance, most games). And writing FPU code is now no longer any more difficult than declaring type float. The compiler handles all the tricky parts.
I think GPGPU will follow a similar trajectory. Right now, we're in phase one. Use a GPU for general purpose computation is such an incredible pain that only the most specialized applications are going to use GPGPU capabilities. High level languages haven't really evolved to take advantage of these capabilities yet. And yes, it's not as though computer scientists don't have appropriate abstractions that would make coding for GPGPU vastly easier. Eventually, GPGPU will become an optional part of the CPU. Eventually high level languages (in addition to the C family, perhaps FORTRAN or Matlab or other languages used in scientific computing) will be extended to use GPGPU capabilities. Standards will emerge, or where hardware manufacturers fail to standardize, high level abstraction will sweep the details under the rug. When this happens, many more applications will begin to take advantage of GPGPU capabilities. Even further down the road, GPGPU capabilities will become bog standard, at which point will see an explosion of applications that need these capabilities for decent performance.
Granted, the curve for GPGPU is steeper because this isn't just a matter of different instructions, but a change in memory management as well. But I think this kind of transition can and will eventually happen.
hat must be why nVidia has decided to enter the x86 chip market and Intel has significantly improved their GPU offerings, as well as indicate they may include vector units in future chips, because these companies plan to work together in the future! It's so obvious! I wish I hadn't paid attention these past 6 months, as it's clearly confused me!
Sarcasm suits you well.
While Intel and nVidia may both be independently reinventing the wheel right now, neither seems to be getting very far very fast. Intel's video offerings have been poor at best and no one has seen an nVidia x86 processor. AMD has already demo'd a prototype, which means they are further along with this Fusion than both Intel and nVidia combined. I don't think it will take long for the decision makers at both of these companies to realize that the other has the missing component.
Of course, you could be right. This is pure speculation on my part and I am pretty much talking from my ass. Still, the idea makes perfect sense to me.
There is no "I disagree" mod for a reason. Flamebait, Troll, and Overrated are not substitutes.
Even if Nvidia's CUDA is as hard as the Ars Technica article suggests, I still hope AMD either makes their chips binary compatible, or makes a compiler that works for CUDA code.
From what I saw at the demo, the AMD stuff was running under Brook. As far as I've been able to make out from nVidia's documentation, CUDA is basically a derivative of Brook that has had a few syntax tweaks and some vendor-specific shiny things added to lock you in to nVidia hardware.
What would Lemmy do?
So I take it that AMD will be ready for Vista's successor?
Well, there's spam egg sausage and spam, that's not got much spam in it.
I laugh every day at the tags people assign to articles, but today I laughed the hardest with the tag "dickinabox" ...
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