Slashdot Mirror
Using GPUs For General-Purpose Computing
Paul Tinsley writes "After seeing the press releases from both Nvidia and ATI announcing their next generation video card offerings, it got me to thinking about what else could be done with that raw processing power. These new cards weigh in with transistor counts of 220 and 160 million (respectively) with the P4 EE core at a count of 29 million. What could my video card be doing for me while I am not playing the latest 3d games? A quick search brought me to some preliminary work done at the University of Washington with a GeForce4 TI 4600 pitted against a 1.5GHz P4. My Favorite excerpt from the paper:
'For a 1500x1500 matrix, the GPU outperforms the CPU by a factor of 3.2.' A PDF of the paper is available here."
At my work place, I'm looking into using the GPUs to do video analysis. Things like cut-scene detection, generating multi-resolution versions of a video frame, applying video effects and other proprietary technologies that were previously done in CPU. The combination of pixel shaders and floating-point buffers really make GPUs a Super-SIMD machine if you know how to exploit it.
www.rexguo.com - Technologist + Designer
No, it's like using your pop-up camper for storage space when you're using it on holidays.
Does anybody know of pointers to papers/research pertaining to using GPUs to perform digital signal processing for, say, real-time audio? Replies would be much appreciated.
What's interesting with new video cards it's their memory capacity, 128 or 256 MB and that this memory is accessible on some new cards at 900 MHz with a data path of 256 bit (which is a lot faster than a CPU with DDR 400 installed).
All that processing power, and the latest games still run at about 22 frames per second, if that.
The CPU can do six billion instructions a second, the GPU can do 18 billion, and every last cycle is being used to stuff a 40MB texture into memory faster. What a waste. Yeah, the walls are even more green and slimy. Whoop-de-fucking-do.
Would it be great if all that processing power could be used for something other than yet-another-graphics-demo?
Like, maybe some new and innovative gameplay?
Business isn't willing to pay for products, innovation and careers, so we get brands, mortgage commercials and layoffs.
At my work we do audio stuff. It would be really neat if I could do some of the more complicated audio analysis (FFT etc) that requires lots of vector math using the video cards gpu. There is probably even some way you could sync the timing for multimedia stuff.
I know nothing about CPU design though
http://www.gpgpu.org/ is a great resource for general purpose graphics processor usage.
Apple's Newton had no CPU, only a GPU that was more than adequate.
Ideas like these are good in general. I'd like to see the industry move away from the CPU-as-chief status quo. Amigas were years ahead of their time in large part because the emphasis wasn't as much on central processing. The CPU did only what it was supposed to do -- hand out instructions to the gfx and audio subsystems.
Hardly using a "motorcycle to tow a pop-up camper." If anything, the conventional wisdom is, "when all you have is a hammer, everything looks like a nail."
...Several indies and companies figure out how to use the powerful GPU's in an efficient manner that would benefit everyone who uses computers on a daily basis and improves the usefulness of the computer making it the best thing in the world again then some greedy bastard comes along flashing his granted patent by the U.S. Patent Office which makes us all screwed...
;)
Ohh well the idea was good while it lasted.
This space is not for rent.
a beowulf cluster of them.
seriously, we have a 16 node beowulf cluster and each node has an unnecessarily good graphics card in them. a lot of the calculations are matrix-based e.g. several variables each 1xthousands (1D) or hundredsxhundreds (2D).
how feasible and worthwhile do you think it would be to tap into the extra processing power?
Some dude wrote Frogger almost entirely in pixel shaders. http://www.beyond3d.com/articles/shadercomp/result s/ (2nd from the bottom).
Forget thrust, drag, lift and weight. Airplanes fly because of money.
From the link you mentioned: "while Apple used a compiler you've never heard of (at least in the x86 world)."
My understanding is that they used GCC.
Further, "Another said that some version of Linux had to be used to compare apples to apples. Well, MacOS X isn't Linux, and the desktop standard for x86 machines is Windows (not that using a properly optimized Linux bothered the Opterons very much). You want to know what machine is fastest, you test in their native environment."
Oh, silly me. Processors are so obviously made to run only one operating system!
I'll take this site's info with a grain of salt.
There's some good stuff in there.
However, it seems a few organisations have actually beaten us to it.
Apple, for example, uses the 3d aspect of the GPU to accelerate its 2d compositing system with quartz extreme. Microsoft, as usual, announced the feature after Apple shipped it, and with any luck Windows users might have it by 2007
-- james
I did a paper on the topic of general-purpose GPU programming for my parallel computing course just this last semester here, interestingly enough. I believe our research indicated that even a single PCI card was so badly throttled by the bus throughput that it was basically useless. AGP does a lot better taking data in, but it's still pretty costly sending data back to the CPU. I have a feeling your proposed setup will be a whole lot more feasible if/when PCI Express becomes mainstream.
With Dual Core CPU's going to be the norm, why not a Dual Core GPU for even faster gfx cards? With everyone wanting 16x antialiasing at 1600x1200 to get over 100fps, its gonna take some very powerful GPU's (or some dual cores).
Even with the ATI 800XT, 1600x1200 can dip below 30FPS with AA/AF on higher settings. Still a ways to go for that full virtual reality look.
This concept was being used back in 1988. The Commodore 64 (1mhz 6510, a 6502 like micro processor) had a peripheral 5.25 disk drive called the 1541, which itself had a 1mhz 6510 cpu in it, connected via. a serial link.
It became common practice to introduce fast loaders: these were partially resident in the C64, and also in the 1541: effectively replacing the 1541's limited firmware.
However, demo programmers figured out how to utilise the 1541: one particular demo involved uploading program to the 1541 at start, then upon ever screen rewrite, uploading vectors to the 1541, which the 1541 would perform calculations in parallel with the C64, then at the end of the screen, the C64 fetch the results from the 1541, and incorporate them into the next screen frame.
Equally, GPU provides similar capability if so used.
You're absolutely correct that these "game snobs" are looking at the past through rose-colored graphics, forgetting all of the stinkers of yesteryear. However, it's not just games where this applies. How many times have you heard people complain about how bad movies are now, or music, or books? It's exactly the same phenomenon. When your grandfather tells you how much better things were "back in the day", it's for exactly the same reason. He's looking back at all the good things, while ignoring all of the bad.
Face it, everything mostly sucks. It always has, and it always will. There will always be some gems that really stand out, and those will be what are remembered when people fondly look back on "the old days". Get over it.
What's really needed is to couple the GPU and CPU in such a way that the GPU actually runs a very low level O/S, like an L4Ka style kernel (http://l4ka.org/), and becomes "just another" MP resource.
Then, on top of this low level, actually runs the UI graphics driver and so on. Other tasks can also run, but ultimately the priority is given to the UI driver.
Then, the O/S on the CPU needs to be able to know generally how to distribute tasks across to the GPU. Fairly standard for a tightly coupled MP that has shared bus memory.
Why do I say this? Because the result is
(a) if you're using an especially high performance application, the GUI runs full throttle dedicated to rendering/etc and acts as per normal;
(b) if you're not, e.g. such as when running Office or Engineering other compute intensive tasks (e.g. recoding video without displaying the video), then the GPU is just another multi processor resource to soak up cycles.
Then, CPU/GPU is just a seamless computing resource. The fantastic benefit of this is that if the O/S is designed properly, then it could allow simply buying/plugging in additional PCI (well, PCI probably not good because of low speed, perhaps AGP?) cards that are simply "additonal processors" - then you get a relatively cheaper way of putting more MP into your machine.
Just look at the matrix multiplication case. Look at the graph and see that 1000x1000 takes 30 seconds on CPU and 7 seconds on GPU. Let's translate it to Millions of operations per second: CPU -> 33 Mop/s, GPU -> 142 Mop/s Matrix multiplication has cubic complexity so for CPU: 1000 * 1000 * 1000 / 7 seconds / 1000000 = 33 Mop/s
Now think a while: 33 million operations on 1.5 GHz Pentium 4 with SSE (I assume there is no SSE2). Pentium 4 has fuse multiply-add unit which makes it do two ops per clock. So we get 3 billion ops per second peak performance! What they claim is that the CPU is 100 times slower for matrix multiply. That is unlikely. You can get 2/3 of peak on Pentium 4. Just look at ATLAS or FLAME projects. If you use one of these projects you can multiply 1000 matrix in half a second: 14 times faster than the quoted GPU.
Another thing is the floating point arithmetic. GPU uses 32-bit numbers (at most). This is too small for most scientific codes. CPU can do 64-bits. Also, if you use 32-bits on CPU it will be 4 times as fast as 64-bit (SSE extension). So in 32-bit mode, Pentium 4 is 28 times faster than the quoted GPU.
Finally, the length of the program. The reason matrix multiply was chosen is becuase it can be encoded in very short code - three simple loops. This fits well with 128-instruction vertex code length. You don't have to keep reloading the code. For more challenging codes it will exceed allowed vertex code length. The three loop matrix multiply implementation stresses memory bandwidth. And CPU has MB/s and GPU has GB/s. No wonder GPU wins. But I can guess that without making any tests.
Some day you may be able to Fold proteins with your GPU.
Doesn't anybody find it annoying that 3-D operation is being hardwired into the video card to begin with? Why aren't we making 200million transistor math coprocessors with high bus speeds, uncoupled from the video card. This way we wouldn't have to keep getting a new video card every time we want to upgrade our systems 3-d performance. Since these operations are highly symmetric, you could put in an array of these into one machine to incrementally upgrade. Also, this would make the issue of how to access your GPU to use for other purposes irrelevant, as it would be a math coprocessor expected to be used as such anyways. And the best reason for doing it this way: OpenGL (and DirectX too) could become more of a thick software layer on top of the generic coprocessor, and since the coprocessors would eventually standardize on common instruction set, you wouldn't need a new version of OpenGL or DirectX for every new coprocessor release. What do you guys think?
Occam's razor is the blind faith in the natural selection of least resistance and in universal oversimplification. -- EF