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Impressive GPU Numbers From Folding@Home
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?"
Are ASICs really that much better than general-purpose circuits?
Generally ASICs are much better than general-purpose circuits except in general cases.
ASCII silly question, get a silly ANSI.
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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
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GPUs are, for the most part, highly specialized parallel computers. 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.
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.))
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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.
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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?
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.
UTF are you talking about? I'm quite sure the mods are not latin-1 post like this go unmoderated.
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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).
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.
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