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Impressive GPU Numbers From Folding@Home

Posted by kdawson on Fri Oct 13, 2006 05:20 PM
from the that's-fast dept.
Supercomputing Hardware
ludd1t3 writes, "The Folding@Home project has put forth some impressive performance numbers with the GPU client that's designed to work with the ATI X1900. According to the client statistics, there are 448 registered GPUs that produce 29 TFLOPS. Those 448 GPUs outperform the combined 25,050 CPUs registered by the Linux and Mac OS clients. Ouch! Are ASICs really that much better than general-purpose circuits? If so, does that mean that IBM was right all along with their AS/400, iSeries product which makes heavy use of ASICs?"
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  • Those 448 GPUs outperform the combined 282,111 CPUs registered by the Linux and Mac OS clients. Ouch! Are ASICs really that much better than general-purpose circuits? If so, does that mean that IBM was right all along with their AS/400, iSeries product which makes heavy use of ASICs?"

    That's pretty lopsided, but I suppose some of it could be explained away by GPU's not chewing through OS code and having to play nice for memory, so they'd be a bit more efficient. Could be most of those Linux and MacOS s

    • Re:Lopsided Alright.. (Score:4, Insightful)

      by blahplusplus (757119) on Friday October 13 2006, @05:25PM (#16431183)
      I have a feeling this is memory bandwidth related, modern GPU's have insane amounts of memory bandwidth compared to the wide range of desktops. Not to mention the parallelism.
      • And I have a feeling that this is also use-related. There are a lot more things which demand processor power than GPU power. I bet there are tons more spare cycles on a GPU than on a CPU. I mean really - what maxes out a GPU? I'm guessing just a handful of games, while many other things rely on the CPU quite heavily.
        • I don't really think that's the reason. GPUs have very fast RAM, lots of it, are dedicated to specific tasks and are very fast at these specific tasks. CPUs have lots of stuff in there that does nothing related to what the GPU is designed for like logic circuits and whatnot. Dedicated hardware is always way better than general purpose hardware.

        • Re:Lopsided Alright.. (Score:5, Informative)

          by throx (42621) on Friday October 13 2006, @06:01PM (#16431541) Homepage
          It has nothing to do with memory bandwidth or use. The ASIC is about 1000 times faster than the CPU because it is using dedicated hardware designed to run very fast and parallel in 3D image processing, which is almost exactly the same problem as folding protiens.

          Unless you are saying all CPUs are pegged at 99.9% use, or the GPU has memory three orders of magnitude faster then you're just looking at a effects that make a few percent difference here and there. The simple fact is the GPU is insanely faster at solving specific problems (3D processing) while it simply cannot ever run an operating system.
        • i'm sure load difference is part of the picture. but load can't be a dominating factor, because while i'm sure there is a load difference it doesn't seem likely that it even approaches the same 50x difference that the performance gap has (if a non-Folding CPU averaged only 2% of its cycles unused, the GPU would have to be used 0% of the time to get a 50x difference).

          i'm guessing that the better parallelization in the GPU together with the fact that the average GPU participating in Folding is much more mo

      • Re:Lopsided Alright.. (Score:5, Insightful)

        by Majik Sheff (930627) on Friday October 13 2006, @08:03PM (#16432647) Journal
        Look at the first two letters of the acronym: Application Specific. A screwdriver and a swiss army knife will both turn a screw, but the screwdriver is going to be much more efficient at it. GPUs are finely tuned to rip through massive volumes of floating point vectors and not much else. It just so happens that the folding project also fits this desctiption and as such is an excellent use of an otherwise wasted resource.

    • Re:Lopsided Alright.. (Score:4, Interesting)

      by oringo (848629) on Friday October 13 2006, @06:17PM (#16431711)
      You can look at the statistics many ways. Here's the GFLOP/CPU catagorized by OS:

      1. GPU: 65.463
      2. Linux: 1.219
      3. Windows: 0.948
      4. Mac: 0.511

      Of course, GPU beating the hell out of CPU in such tests is no surprise. It's pretty much a massive parallel vector engine. I'm more interested in seeing how PS3 holds agains all other guys when it comes out. They have a folding client for PS3 already.
        • Man, imagine how simple-minded and yet weak superhuman an AI that ran solely on GPUs would be?

          "I can think of many, many things at once. As long as they are the same type of thing."
  • Everyone knows that ASCII text is faster than binary.

  • Are ASICs really that much better? (Score:5, Funny)

    by Anonymous Coward on Friday October 13 2006, @05:23PM (#16431159)
    Are ASICs really that much better than general-purpose circuits?

    Generally ASICs are much better than general-purpose circuits except in general cases.
    • Exactly. The original question is stupid. An ASIC can be orders of magnitude faster for an fft, but you can't write a word processor for it.

      • Re: (Score:3, Funny)

        by Jeremi (14640)
        An ASIC can be orders of magnitude faster for an fft, but you can't write a word processor for it.


        You realize that somebody is bound to write a word processor that runs on a GPU now, just to prove it can be done?

    • Re: Are ASICs really that much better? (Score:4, Insightful)

      by Jeffrey Baker (6191) on Friday October 13 2006, @06:07PM (#16431621)
      Good one ... but I also wonder why anyone is throwing around the term "ASIC" in this article. A GPU is obviously not an application-specific circuit, which is clearly shown by the fact that it can be programmed to process graphics, or protein folding, or numerous other tasks. A GPU is a general-purpose processor like a CPU, it just happens to have different numbers and kinds of execution units.
      • Partially true. The GPUs of today now have some general purpose circuits, but they are far from optimized and the execution unit count is skewed to the point that these processors would never, ever be able to run, say, an OS with anything approaching efficiency. FAH benefits from the insane amount of Floating point power because FAH is nothing but a pure FP stress test. They had to heavily modify the code to run on these babies, basically tuning the problems into vector information and letting the GPU do its thing, throwing. Only a few areas involve a need for CPU style processor, which is functionality provided only on these new cards. So please, please realize that even though these cards do not a contain a "protein folding circuit", they did modify the program to run on what it does have: 4x4 matrix operation units for multiplication and addiction.
        • they did modify the program to run on what it does have: 4x4 matrix operation units for multiplication and addiction.
          This is also what my mother was always telling me when I was a boy: "if you play video games you'll become an addict". She was so right.

  • IBM right all along, or obvious? (Score:3, Funny)

    by Gothmolly (148874) on Friday October 13 2006, @05:24PM (#16431165)
    Custom app written to run on hardware specifically designed to run apps like it, outperforming general purpose CPUs? Newsflash from Ric Romero!!1!

  • Distributed amongst home users (Score:5, Funny)

    by Skevin (16048) * on Friday October 13 2006, @05:24PM (#16431169) Journal
    So, will someone please create a really pretty 3D screensaver representing the folding calculation process? I'd love to see a represention with hi-res lighting and texturing, full transforms, and user-scalable views at 400 million triangles/sec.. Thanks.

    Solomon

  • Maybe I'm missing some subtlety in the OP somewhere, but if GPUs weren't better at what they're doing than CPUs, there wouldn't be a point in having a GPU in the first place.

    ...and if you have a problem that can be expressed in terms of the problem space the GPU is designed to handle, then that problem is going to run faster on the gpu than on the CPU.

  • The purpose a general purpose CPU is to handle all calculations. For this task, which is very specific, a GPU may be that much better.

  • GPUs are Specialized Parallel Computers (Score:5, Insightful)

    by ThinkFr33ly (902481) on Friday October 13 2006, @05:34PM (#16431273)
    GPUs are, for the most part, highly specialized parallel computers [wikipedia.org]. Virtually all modern CPUs are serial computers. They do essentially one thing at a time. Because of this, most modern programming languages are taylored to this serial processing.

    Making a general purpose parallel computer is very, very hard. It just so happens that you can use things like shaders for more than just graphics processing, and so via OpenGL and DirectX you can make GPUs do some nifty things.

    In theory, and indeed often in practice, parallel computers are much, much faster than their serial counterparts. Hence the reason a GPU that costs $200 can render incredible 3D scenes that a $1000 CPU wouldn't have a prayer trying to render.

  • This is the perfect time... (Score:3, Interesting)

    by loraksus (171574) on Friday October 13 2006, @05:36PM (#16431311) Homepage
    ... to start heating your house with your computers ;)

    I actually installed boinc with seti on several of my machines last night and it worked quite well to heat part of the house (us Canadians need to turn the heater on earlier). Took a bit of time to get started, but it was nice and toasty in the morning.

    Does anyone know if this method is less efficient in generating heat than using a apace heater? Slower perhaps...
    If you're going to use energy by turning on the wall heater anyways, why not use it to crunch some numbers?
    • Does anyone know if this method is less efficient in generating heat than using a apace heater?

      It probably depends on the technology your space heater uses to generate heat. If it's old-fashion resistive coils, it's probably about as efficient. The newer ceramic-element heaters I'm not sure about.

      For the most efficient electric heating, you should be using a whole-house heat pump.
        • P.S. - all electric heaters have the same efficiency, assuming no energy is "wasted" as visible light. The difference between them basically comes down to radiant vs. convection heat. Which is more useful depends on your circumstances. Radiant heat has the advantage of heating you and not the air.

          I'm not sure if you meant to exclude heat pumps from this statement, but if not, heat pumps can achieve 3-4 times the efficiency of resistance heaters. Here's a handy link [gsu.edu] that explains it in layman's terms.

          But yo

    • I actually installed boinc with seti on several of my machines last night and it worked quite well to heat part of the house (us Canadians need to turn the heater on earlier). Took a bit of time to get started, but it was nice and toasty in the morning. Does anyone know if this method is less efficient in generating heat than using a apace heater? Slower perhaps..


      Using your CPU as a space heater is not a bad idea. It is 100% efficient. Every watt it consumes gets turned into heat. Before someone says "but the cooling fans are wasteful" let me remind you that the air moved by those cooling fans will eventually come to a stop (inside your house) as a result of friction, releasing its energy as heat in the process.

      Depending on what type of space heater you use, and the construction of your house, your computer can be more efficient than many other electric space heaters. Since none of the energy "consumed" by your CPU/GPU is converted to visible light, none of it has the opportunity to leave your house through your window panes (assuming you have IR reflective glass). Contrast this to quartz and halogen space heaters which produce a fair amount of visible light.

      In much the same way, incandescent bulbs match the efficiency of compact fluorescents during the winter months. Every watt "wasted" as heat during the summer is now performing useful work heating your house. (Before someone says "you called a quartz/halogen space heater inefficient because of its waste light, and now an incandescent efficient because of its waste heat!' let me say that the space heater's light is not useful light, while the bulb's heat is useful heat (during the cool months.))

      • Re:This is the perfect time... (Score:4, Informative)

        by evilviper (135110) on Friday October 13 2006, @07:14PM (#16432271) Journal
        It is 100% efficient.

        Not true. You aren't taking into account Power Factor at all... Not that I'm surprised, as most people don't understand it.

        With switching power supplies, it's common to see PF in the range of 0.4, as opposed to fully-resistive electric space-heaters (and incandesent lightbulbs) with a perfect 1.0 PF.

        Residential customers are lucky, in that they don't get charged for PF losses by the power company, while companies certainly do. However, it's still highly ineffecient, even if you aren't paying for it directly.

        And besides that, electric heating is almost always more expensive than conventional heating, like natural gas, or electric heatpumps.

          • Re: (Score:3, Informative)

            by evilviper (135110)
            PF "losses" are not losses, it is power that is in effect returned back to the source. One can simply treat it as power that isn't delivered at all.

            Electricity isn't water, you can't return it to the source.

            With a lower power factor, you're either forcing the power company to install huge banks of capacitors, or making the generators work that much harder for fewer watts actually delivered/used. That's practically the definition of "inefficent".

            • Re: (Score:3, Informative)

              by Agripa (139780)
              In the case of computer power supplies that use a rectifier and capacitor combination for AC to DC conversion which is almost all of them, they do not look like an inductive or capacitive load but have a lower power factor caused by drawing current in pulses instead of a sine wave. The result is a higher RMS current then necessary for the load which causes increased line losses and requires higher current capacity for a given power. In extreme cases, distribution transformers can go into saturation causin

      • Not really. (Score:4, Insightful)

        by TerranFury (726743) on Friday October 13 2006, @08:41PM (#16432867)

        >Using your CPU as a space heater is not a bad idea. It is 100% efficient.

        Not really. Consider exergy [wikipedia.org]. Yes, your CPU is just as efficient as any electric space heater. However, consider that the alternative is probably burning natural gas or oil in a furnace. If you burn fuel for heat, 90%+ of the chemical energy goes to producing heat (the rest is lost as unburnt hydrocarbons in the exhaust). If you burn fuel to spin a turbine at a power plant, only about 40% goes to electrical energy, and unless it's a cogeneration plant which uses the waste heat for industrial purposes, the rest is lost as heat up the smokestacks. So, starting from the fossil-fuel source, electrical heating is less than half as efficient as burning fuel for heat. If you do need to heat using electric power, it's much more efficient to use that electricity to pump heat in from a lower temperature outside than it is to turn that electricity itself into heat.

        If you are stuck with electric (non-heat-pump) heating in your house, however, you are correct: There is absolutely no reason not to run your CPU or any other electrical appliance full tilt.

    • Re: (Score:3, Insightful)

      by olddoc (152678)
      If my computer idles at 150w and runs FAH at 100% cpu at 200w and I need 20h to generate 1 unit,
      I am spending $.10 for the extra kw hour roughly. In the summer I waste money on AC in the winter I save
      gas money on heat. If I put my computer in 4watt S3 standby for 15 of those 20 hours, I can save a lot more.
      FAH calculations do not depend on "free" "idle" computer power, they depend on users spending money to generate
      the results.
      • Well, I really, really hate the "heater that hasn't been used for 6 months" smell, so that was sort of my primary focus.
        That makes it a little better... right?
        Please?
  • Q: Are ASICs really that much better than general-purpose circuits?
    Yes, that's why anyone would bother.
    Q: If so, does that mean that IBM was right all along with their AS/400, iSeries product which makes heavy use of ASICs?
    A: Yes and no. More relevant will Cell pave the way to good price/performance. The problem with the iSeries line is not so much performance, but price/performance For the same cost of an iSeries config you can cluster a bunch of xSeries and beat it through sheer brute force of CPUs. I
    • Not completily true.

      I run using both hardware types. xSeries can not complete in the shear thoughput of grunt data processing - Billions and Billions of records. Yes, 1000 PC can process billion of records, but then your cost pass that of 1 iSeries.

      Now when you limit the processing to the type of computer that best handles that type of work:

      xSeries is better in single/one off processes, like a web page request, or finding the Lat/Long of Address, where all the information is fresh look up each time. So s
  • Are there actual benchmarks yet comparing average time per WU for GPU vs CPU?

    For all we know the majority of those Linux and Mac clients are old P2s and G3s.

  • If the same processor can be used to generate eye candy and cure cancer, I wouldn't call it application specific.

  • Not a mystery (Score:3, Insightful)

    by DaveJay (133437) on Friday October 13 2006, @06:25PM (#16431767)
    Take one hundred people with computers, and who have an interest in Folding@Home. Offer them a CPU-driven version of the app, and 100 computers will be running the CPU-driven app, regardless of the age/performance of the machine.

    Now, offer them a GPU-driven alternative. For the most part, the only people that will install and run it are those with a fancy-schmancy video card capable of running it, and for the most part, the only people that have a fancy-schmancy video card capable of running it have high-performance computers as well (or at least more recent computers that came with compatible cards.)

    So let's say that's ten out of the hundred, and those ten are statistically likely to have had the highest-performing CPUs as well; so you've pulled the top ten performers out of the CPU-client pool, and thrown them in the GPU-client pool. Even if you didn't switch those ten people over to the GPU, you could probably isolate those computers' CPU-client performance numbers from the other 90 and find that they're disproportionally faster than a larger number of the slower computers.

    There's still more to the story, of course, but you really are taking the highest-performing computers out of the CPU pool and into the GPU pool. The exception would be high-performance servers with lousy/no graphic cards, but those are likely working so hard to perform their normal business that Folding@Home isn't a priority.

    • Re:Not a mystery (Score:5, Insightful)

      by tkittel (619119) on Friday October 13 2006, @07:56PM (#16432595)
      Your logic is fine, but you are overestimating the effect you mention if you really think that it "solves the mystery".

      500 users out of 25000 means that you have at most taken the 2 percent highest performers out of the CPU pool. If we assume that those 2 percent have computers that are 5 times as powerful as the average computer, then we have lowered the average performance of the CPU pool by roughly 9%.

      This 9% systematic effect will lower the reported performance superiority of around 5000% of the GPU vs. the CPU to something like 4500%. I.e. it doesnt change the result at all (which seems to be that GPUs kick ass for these applications).

  • Move the vector processor on-board? (Score:4, Interesting)

    by Zygfryd (856098) on Friday October 13 2006, @06:33PM (#16431861)
    So when are we going to see (x86/64) motherboards with a socket for a standard processor and a socket for a vector processor?
    Couldn't we finally have graphics cards that only give output to the screen and separate vector processors with a standardized interface / instruction set?

  • Remember: 1 GPU has more than one processor. (Score:5, Informative)

    by nick_davison (217681) on Friday October 13 2006, @06:42PM (#16431981)
    X1900 - 48 pixel shader processors plus 8 vertex shaders. Assuming you manage to run them all equally in parallel: 56 processors.

    Standard CPU - 1 core (assuming dual cores get read as 2 CPUs).

    448 GPUs x 56 = 25,088 effective processors all with on card memory.

    25,050 CPUs x 1 core = 25,050 effective processors all dealing with system busses etc.

    In short, if you're performing one simple task trillions of times, many very simple, highly optimized processors with dedicated memory do the job better than even a similar number of much more capable processors that have to play nice across a whole system.

    And this ignores the number of old couple of hundred megahertz systems that people don't use anymore so hand over to the task vs. X1900s being the very high end of ATIs most recent line.

    For massively parallel tasks like rendering pixels, folding proteins, compressing frames of a movie, etc. I'd absolutely love large quantities of a simple processor. For most other tasks, given present technology, I'd still side with fewer more able processors. Either way comparing 448 of something with 56 processors within it to 25,000 single processors and saying, "But 448 is SO much less than 25,000!" is an unfair comparrison.

  • Actually Linux users have good hardware: (Score:4, Informative)

    by olddoc (152678) on Friday October 13 2006, @09:41PM (#16433093)
    Look at the number of Tflops per active cpu by OS.
    I took (TFlop/active cpu)*1000 to get a readable number --
    or Gflops/cpu
    Windows is .948
    Mac is .51
    Linux is 1.21
    And GPU is 65!

    The source:
    http://fah-web.stanford.edu/cgi-bin/main.py?qtype= osstats [stanford.edu]

    The average Linux user proably has a decent AMD Athlon,
    The average Windoze user has a P4 Dell.
    Athlons just crunch the math better.

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