NVIDIA's 1.4 Billion Transistor GPU: GT200 Arrives as the GeForce GTX 280 & 260
by Anand Lal Shimpi & Derek Wilson on June 16, 2008 9:00 AM EST- Posted in
- GPUs
Folding@home Now on NVIDIA
Folding@home, for those who don't know, is a distributed computing app designed to help researchers better understand the process of protien folding. Knowing more about how protiens assemble themselves can help us better understand many diseases such as alzheimers, but protien folding is very complex and takes a long time to simulate. the problem is made much easier by breaking it up into smaller parts and allowing many people to work on the problem.
Most of this work has been done on the CPU, but PS3 and AMD R5xx GPUs have been able to fold for a while now. Recently support for AMD's R6xx lineup was added as well. NVIDIA GPUs haven't been enabled to run folding@home until now (or very soon anyway). Stanford has finally implemented a version of folding@home with CUDA support that will allow all G80 and higher hardware to run the client.
We've had the chance for the past couple days to play around with a pre-beta version of the folding client, and running folding on NVIDIA hardwarwe is definitly very fast. Work units and protiens are different on CPUs and GPUs because the hardware is suited to different tasks, but to give some perspective a quadcore CPU could simulate tens of nanoseconds of a protien fold, while GPUs can simulate hundreds.
While we don't have the ability to bring you any useful comparative benchmarks right now, Stanford is working on implemeting some standard test cases that can be run on different hardware. This will help us actually compare the performance of different hardware in a meaningful way. Right now giving you numbers to compare CPUs, PS3s, AMD and NVIDIA GPUs would be like directly comparing framerates from different games on different hardware as if they were related.
What we will say is that NVIDIA predicts that the GTX 280 will be capable of simulating something between 5 and 6 hundred nanoseconds of folding per day while CPUs are going to be two orders of magnitude slower. They also show the GTX 280 handily ahead of any current AMD solutions by high margins, but until we can test it ourselves we really don't want to put a finer point on it.
In our tests, we've actually seen the GT200 folding client perform at between 600 and 850ns per day (using the timestamps in the log file to determine performance), so we are quite impressed. Work units complete about every 20 to 25 minutes depending on the protien and whether or not the viewer is running (which does have a significant impact since the calculations and the display are both running on the GPU).
Hardware H.264 Encoding
For years now both ATI and NVIDIA have been boasting about how much better their GPUs were for video encoding than Intel's CPUs. They promised multi-fold speedups in performance but never delivered, so we've been stuck encoding and transcoding videos on CPUs.
With the GT200, NVIDIA has taken one step closer to actually delivering on these promises. We got a copy of a severely limited beta of Elemental Technologies' BadaBOOM Media Converter:
The media converter currently only works on the GeForce GTX 280 and GTX 260, but when it ships there will be support for G80/G92 based GPUs as well. The arguably more frustrating issue with it today is its lack of support for CPU-based encoding, so we can't actually make an apples-to-apples comparison to CPUs or other GPUs. The demo will also only encode up to 2 minutes of video.
With that out of the way however, BadaBOOM will perform H.264 encoding on your GPU. There is still a significant amount of work being done on the CPU during the encode, our Core 2 Extreme QX9770 was at 20 - 30% CPU utilization during the entire encode process, but it's better than the 50 - 100% it would normally be at if we were encoding on the CPU alone.
Then there's the speedup. We can't perform a true apples-to-apples comparison since we can't use BadaBOOM's H.264 encoder on anything else, but compared to using the open source x264 encoder the performance speedup is pretty good. We used AutoMKV and played with its presets to vary quality:
In the worst case scenario, the GTX 280 is around 40% faster than encoding on Intel's fastest CPU alone. In the best case scenario however, the GTX 280 can complete the encoding task in 1/10th the time.
We're not sure where a true apples-to-apples comparison would end up, but somewhere between those two extremes is probably a good guesstimate. Hopefully we'll see more examples of GPU based video encoder applications in the future as there seems to be a lot of potential here. Given how long it takes to encode a Blu-ray movie, we needn't even explain why it's necessary.
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epobirs - Monday, June 16, 2008 - link
There is an important precedent that gives Nvidia good reason to not rush to a new smaller process level. Recall when ATI first became a serious player in gaming GPUs with the 9700. It was for its time a big chip pushing the limits of the process level, while Nvidia at the time was concentrating on bleeding edge technology. Nvidia's chips got stomped by ATI's in that generation, in large part because the ATI chip had far better optimization of its transistors.anartik - Monday, June 16, 2008 - link
We can agree the pricing sucks. But the point that seems to be missing is that Nvidia promised a 50% performance improvement and they delivered. The 280 delivers 45FPS vs 32FPS for the 9800GT in Assassins creed. Thats just shy of 50% (48FPS) which is a huge performance increase compared to what we have been getting the past couple years for a new card. Slap 2x280 on a card and it vaporizes the 9800 GX2 or any SLI/Xfire solution. The 9800 GX2 scales ~63% over the 9800GTX. So if you do that for a 280GX2 (or SLI) you get roughly 73 frames per second. Plus the new cards have more memory to deal with bandwidth and large textures vs the nuetered 512 on the 96/9800's and 8800GT... the reason I have held onto my 8800 GTX with 768mb. Granted I won't be rushing out and buying one tomorrow but the 280 is the fastest GPU and a x2 will be faster than any other x2 card. It's a little rediculous to think the single 280 sucks becuase it's not faster than multiple GPU's like the 9800 GX2 (although when memory counts it is).araczynski - Monday, June 16, 2008 - link
this is all they could spit out? all that noise and all those transistors and it gets its ass handed to it by the gx2 more often than not?talk about fizzle, perhaps at best it'll lower the price of the gx2 a bit.
not to mention no built in hdmi, good lord, they must've had two seperate teams working on the gtx and gx2 at the same time and just wanted to see what they could come up with separately.
Ananke - Monday, June 16, 2008 - link
I don't think many people at this forum tread understand that nVidia target is the supercomputer market. I was totally impressed from one post a month ago, where a software engineer managed to put and use 3 SLI system for magnetoresonance rendering. Nvidia and AMD /that's why they acquired ATI/ have already significant experience in multiprocessor and parallel calculation. nVidia is ahead though, since they have CUDA becoming more popular for complex calculation. A year ago Intel realized parallel processing from Sun is their biggest danger, now nVidia and Ati come too. Imagine, supercomputers build with thousands of G200 chips, and only some Intels used for mapping, instead of thousands of Xeons. nVidia thinks way more ahead just for the mere visual/gaming market. I am very very impressed, and very eager to see what ATI can do. Also, I hope Ati and Havoc will be able to offer competition to CUDA, or uniformity? Anyway, from a scientific point of view, recent developments in the graphic market make foundamental science more affordable than anytime before.Reflex - Monday, June 16, 2008 - link
Anand/Derek -I am not sure why you are comparing this chip to a Penryn or other general purpose CPU as the comparisons are meaningless. GPU's are designed very differently than CPU's, namely a high level descriptor language is used and the design is then created by a program, which is then hand tweaked by engineers. By contrast, a CPU may use a high level language, but the actual design is almost entirely done by hand, with large teams working on each sub component and literally years of tweaking. It takes Intel between five and ten years to bring a design to market, which is why there is such a push by them to keep adjusting the design and optimizing it to stretch its usefulness out as long as possible to maximize the initial investment. This simply does not happen with a GPU.
GPU's are designed to last 18-24 months as a competitive solution. nVidia and Ati cannot afford to spend even five years designing them. As a result the level of hand optimization is greatly reduced, and inefficiencies with transistors are tolerated. Typically they are produced on equipment that is already paid for by the previous, more optimized products, or contracted out to third parties(TSMC). Since the products are sold for a premium, the wasted die space is not very relevant. It is a diametrically opposed process to what you see with CPU development.
Despite how impressive it may seem to go on about 1.4 billion transisters, truthfully a modern CPU does more with far less than a modern GPU, and honestly neither nVidia nor Ati are in the same league as Intel and AMD, neither at the engineering level nor when comparing the products they put out. To an Intel engineer, this GPU is at least four times larger than it needs to be to get the performance you get out of it.
The maturation of the industry, either due to reaching a point where GPU's can do 90% of what anyone needs, or simply because power budgets get more restrictive, will come when the level of optimization required for a CPU is required for a GPU, and product cycles stretch out to 3-5 years. Then you will have a more direct comparison between the two, since the design parameters will be much more similiar.
I am not knocking nV here, btw, I'm simply calling into question why one would even compare a Penryn to a GPU, it makes no sense at all when they were designed from the ground up for different purposes, lifespans and with different transister budgets.
7Enigma - Tuesday, June 17, 2008 - link
I think what this shows is there a brute force way of doing something that while not necessarily pretty can get you to a goal. Yes compared to Intel's latest and greatest it is a grotesque abomination of wasted energy/transistors/die size, but the bottom line is it is pretty darn impressive from a CPU/GPU standpoint.I think many of us long for the days of more than 2 major competetors for each race (CPU/GPU). We've been stuck in a rut with ATI and Nvidia, AMD and Intel. Yes you have some niche products by other companies, and budget pieces being made by a host of has-beens, but really tier 1 stuff is just not being fought over by more than 2 companies.
What I want to see (complete dreamland here) is a start up from some very savvy disgruntled employees of say AMD/ATI, Intel, IBM, etc. (and don't forget possibly the most important segment, the marketing team) with some clout and a LOT of dough to say, "Screw this, we're going balls to the wall and throw the kitchen sink at the market."
I mean let's be honest here, what's another 100 watts or a billion transistors anymore? I can guarantee you every geek out there would shell out more money for a product that devestates the current competition. I don't care if it's not as frugal with the power, or as small, or as pretty, I want the speed man, gimme the speed!
Anand Lal Shimpi - Monday, June 16, 2008 - link
While I'd normally agree with you, GPUs have been getting pretty complex to design. Much of the shader multiprocessors in G80 and GT200 were designed by hand, and remember that G80 (the original predecessor to GT200) was in development for four years before its launch.The transistor comparison is a valid one, while Penryn is a very impressive design, it is so for different reasons than GT200. The size of GT200 also helps illustrate fundamental differences in approach to CPU vs. GPU design and really highlights why Intel is building Larrabee.
-A
crimson117 - Monday, June 16, 2008 - link
Because to non-engineers, they're two silicon computer chips, and 1.4 billion of anything is a lot!It also helps me to visually understand why this thing gets so hot, since it's got so much more surface area packed with transistors.
You're right that CPUs and GPUs are designed for different tasks and shouldn't be considered pure apples to apples, but then you go against your own advice and start saying how CPUs are so much more advanced, and how Intel engineers could do that in 1/4 the size of a chip. So which is it - should they be compared, or should they not be compared?
And the authors did mention how simple it could be for either company to slap the other type of chip right in with their usual type; make a Intel CPU with added GPU capabilities, or make a nVidia GPU with CPU capabilities. So there's another point where they recognize the differences but do try to illustrate the sameness.
So I'm not really sure your criticisms hold water.
Reflex - Monday, June 16, 2008 - link
You are looking for contradictions where there are none. A chip is a chip, but that does not mean that they are all designed with the same goals, budgets and time constraints. *IF* Intel devoted the resources to a GPU that they devote to a CPU, yes they could produce a product like this in a fraction of the transisters. That said, the product would take 5-10 years to design, would cost hundreds of millions of dollars to develop, and would need a lifespan of at least 5 years in the market to be worth the effort. Obviously this is not a reasonable approach in a market with such fast product turnover.My post was not an attempt to diss nV or this product, it was pointing out that the comparison of a GPU to a CPU is inane as they have completely different design constraints. You may as well compare a CPU to cache memory, or RAM or a sound processor. All have transisters, right?
It especially bothered me when they implied that nVidia has the transister budget to toss a general purpose CPU on the die. The fact is that they may have the transister budget, but they do not have the time or money available to do so, and the product would be obsolete before it ever hit the market as a result of such an attempt. It would be marrying two completely different design philosophies, and this is why the combined CPU/GPU products that are upcoming are not likely to be the strongest performers.
paydirt - Monday, June 16, 2008 - link
You all seem to be assuming that GPUs will only be used for games. If that's all you care about, then why do you whine when a GPU is made to perform well as a number cruncher (for science, for modeling/simulations)?It's the best single GPU gaming card.
It's the best widely (?) available GPU number cruncher.
For a whole system gaming GPU solution, it isn't the most cost effective.
If you're all into numbers, then why are you assigning emotions to it. It simply is what it is.