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."
Now I finally have a use for the 20 Voodoo 2 cards I have in a box in the basement. Now I can have my very own supercomputer. I just need some six pci slot motherboards.... Instant cluster!
Humor from a Genetically Molested Mind
Intel's been telling me for years that I need faster hardware from THEM to get the job done...
You mean........ they were lying?!?!?
CRAP!
/^[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,4}$/i
http://developers.slashdot.org/article.pl?sid=03/1 2/21/169200&mode=thread&tid=152&tid=18 5
Here's a HTML version of the PDF, thanks to Google.
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
The GPU are very fast ... at performing vector and matrix calculations. This is the whole point. If general computing CPUs were capable of doing vector or matrix calcs very efficiently, we would probably not have GPUs.
The Pentium 4 EE actually has 178 million transistors, which puts it in between ATI's and NVIDIA's latest.
In all of this, keep in mind that there's computing and there's computing...the kind of computing power in a GPU is excellent for doing the same numeric computation to every element of a large vector or matrix, not so much for branchy decisiony type things like walking a binary tree. You wouldn't want to run a database on something structured like a GPU (or an old vector-processing Cray), but something like a simulation of weather or molecular modeliing could be perfect for it.
The similarities of a GPU to a vector processing system bring up an interesting possibility...could Fortran see a renaissance for writing shader programs?
General-purpose computation using graphics hardware has been a significant topic of study for the last few years. Pointers to a lot of papers and discussion on the subject are available at: www.gpgpu.org
No, it's like using your pop-up camper for storage space when you're using it on holidays.
Two words: virtual pr0n
Show me on the doll where his noodly appendage touched you.
Is a course being offered at caltech since last summer on using gpus for numerical work. Course page is here.
:wq
"Utilize the sheer computing power of your video card!"
New market blitz, hmmmm.
SETI ports their code, and within five days their average completed work units increase 1000 fold. 13 hours later, they have evidence of intelligent life at 30000 locations within one degree.
Microsoft gets the hint, and comes out with a brilliant plan to utilize GPUs to speed up their OS and add bells and whistles to their UI.
And, once again, Apple and Quartz Extreme is ignored.
Before you get excited just remember how asymmetric the APG bus is. Those GPUs will be at much better use when we get them as 64bit pci cards.
The whole point of graphic cards is that they have a dedicated purpose. Using the cards for anything that is general purpose is like using a motorcycle to tow a pop-up camper.
What's relevant is that to the processor on a graphics card, its dedicated purpose is simply a bunch of logic. There's no dedicated "this must be used for pixels only, all else is waste" logic inherent in the system. there are MANY purposes for which the same/similar logic that applies in generating 3D imagery can be used, and that seems the purpose of this paper. Run THOSE type operations on the GPU. Some things they won't be able to do well no doubt - but those they can, they can do extremely well.
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
Creating a way to use the specialize GPUs for vector processing that is not graphics related is ingenious. Like a lot of great ideas, it is sooo obvious AFTER you see some one else do it.
Don't miss the point that this is not intended for general purpose computing. Don't port OoO to the graphics chip.
Where it is huge is in signal processing. FPGAs have begun replacing even the G4s in this area recently because of the huge gains in speed vs. power consumption an FPGA affords. However, FPGAs are not bought and used as is, and end up costing a significant amount (of development time/money) to become useful. Being able to use these commodity GPUs for vector processing creates a very desirable price/processing power/power consumption option. If I were nVIDIA or ATI, I would be shoveling these guys money to continue their work.
I am living proof of the Peter Principle
If you have a matrix solver, there is no telling what you can do. And i remember, these papers show that the speed is faster than the matrix calculations of the same stuff using the CPU.
# Linear Algebra Operators for GPU Implementation of Numerical Algorithms
Jens Krüger, Rüdiger Westermann
# Sparse Matrix Solvers on the GPU: Conjugate Gradients and Multigrid
Jeff Bolz, Ian Farmer, Eitan Grinspun, Peter Schröder
# Nonlinear Optimization Framework for Image-Based Modeling on Programmable Graphics Hardware
Karl E. Hillesland, Sergey Molinov, Radek Grzeszczuk
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."
BrookGPU
from the BrookGPU website...
As the programmability and performance of modern GPUs continues to increase, many researchers are looking to graphics hardware to solve problems previously performed on general purpose CPUs. In many cases, performing general purpose computation on graphics hardware can provide a significant advantage over implementations on traditional CPUs. However, if GPUs are to become a powerful processing resource, it is important to establish the correct abstraction of the hardware; this will encourage efficient application design as well as an optimizable interface for hardware designers.
From what I understand this project it aimed at making an abstraction layer for GUP hardware so writing code to run on it is easier and standardsied.
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?
I thought this looked familiar:
1 /169200.shtml?tid=152&tid=185
http://developers.slashdot.org/developers/03/12/2
At least, I would imagine most of the comments would be the same or similar....
Using GPUs For General-Purpose Computing
I'm glad that finally they started to use the General-Purpose Unit. What took them so long?
Sincerely,
Pan Tarhei Hosé, PhD.
"Homo sum et cogito ergo odi profanum vulgus et libido."
Remember the co-processors? Well, actually I don't (I'm a tad to young). But I know about them.
Maybe it's time to start making co-processing add-on cards for advanced operations such as matrix mults and other operations that can be done in parallell on a low level. Add to that a couple of hundred megs of RAM and you have a neat little helper when raytracing etc. You could easily emulate the cards if you didn't have them (or needed them). The branchy nature of the program itself would not affect the performance of the co-processor since it should only be used for calculations.
I for one would like to see this.
Dude, you obviously have never tried to sleep in a motorcycle.
KFG
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.
There is however one thing to keep in mind. Presently our GPU's may have the headroom to play with, but with Apple's Quartz, and Microsoft's Longhorn, let alone what's coming with X. That headroom may disappear, and our video cards will have to go back to being video cards.
On those operating systems that require them, that could very well be.
Still makes a nice thought that a linux box without even X installed, but a kickass graphics card, could crunch away doing something 4 times quicker than any windowed machine.
Perhaps offloading the CPU to the GPU is the wrong way to look at things? With the apparently imminent arrival of commodity (low power) multi-CPU chips, maybe we should be considering what we need to add to perform graphics more efficiently (ala MMX et al)?
While it's true that general purpose hardware will never perform as well as or as efficiently as a design specifically targeted to the task (or at least it better not), it is also equally as true that eventually general purpose/commodity hardware will achieve a price-performance point where it is more than "good enough" for majority.
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
Now I finally understand that acronym: General purpose unit!
Lemme try to help:
a) Not equal. Apples and oranges. A GPU will do repeated calculations very, very fast, like matrix transforms and the like. A CPU on the other hand will make decisions based on input, rather than just crunching numbers
b) The main display (the GUI) already uses many tricks on the graphics card. The hard part is making sure that all graphics cards support the features. Things like the xrender extension and such are becoming more common as graphics cards and drivers get "standard" capabilities
c) Your imagination is the limit as to what it could be used for. Just realize that it's a good data processing unit, not a good program execution unit. Use each for their strengths.
d) Modified? With new cards/drivers, all it takes is OpenGL calls to start taking advantage of this power. All it really takes is someone who knows what they're doing and has a bit of inspiration.
My blog. Good stuff (when I remember to update it). Read it.
Well they already make DSP cards for audio processing. Simply do a google(TM) search for "DSP card" and you will get several vendors.
I can't imagine it would take a whole lot to hack them for just their processing power outside of audio applications.
When I say oh shut the fuck up.
Sorry for the flames, but seriously, I get so damn sick of all the "all new games suck" whiners. Look, there are legit reasons to want new technology. It is nice to have better graphics, more realistic sound, etc. It is NICE to have game that looks and sounds more like reality. Yes, that doesn't make the game great, but that doesn't mean it's worthless.
What's more, don't pretend like all modern games suck while old games ruled. That's a bunch of bullshit. Sure, there are plenty of modern games that suck, but guess what? There are tons of old games that suck too. Thing is, you just tend to forget about them. You remember the greats that you enjoyed or heard about, the ones that helped shape gaming today. You forget all the utter shit that was released, just as is released today.
So get off it. If you don't like nice graphics, fine. Stick with old games, no one is forcing you to upgrade. But don't pretend like there is no reason to want better graphics in games.
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.
What I remember about co-processing cards and "intelligent peripheral cards" (like raid controllers or network cards with an onboard processor) is this:
There is a certain overhead because a communications protocol is to be established between the main processor and the co-processor. For simple tasks the main processor often stops and waits for the co-processor to complete the task and retrieves the results. For more complicated tasks, the main processor continues but later an interrupt occurs that the main processor must service.
You must be very careful or the extra overhead of this communication makes the execution of the task slower than without the co-processor. This is certainly going to happen at some time in the future, when you increase central processor power all the time but keep using the same co-processor.
For example, your matrix co-processor needs to be fed the matrix data, start working, and tell it is finished. Your performance would not only be limited by the processor speed, but also by the bus transfer rate, and by the impact those fast bus transfers have on the CPU-memory bandwidth available and the on-CPU cache validity.
When you are unlucky, the next CPU you buy is faster in performing the task itself.
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.
I've been thinking about using the GPU for audio DSP work for some time, even got to a point where I could transform some signal by "rendering" it into a texture (in a simple way, I could mix two sounds using the alpha as factor).
The problem is that these cards are made to be "write only" and that basicaly fetching back anything from them is *very* slow, which makes them totaly useless for the purpose, since you *kmow* the results are there, but you can't fetch them in an usefull/fast maneer.
I wonder if it's deliberate, to sell the "pro" cards they use for the rendering farms
QE is cool, but it doesn't do anything similar at all to what they're talking about here. FFTs on an NV30 are only incidentally related to texture mapping window contents. Check out gpgpu.org or BrookGPU. In a sense, the idea is to treat modern graphics hardware as the next step beyond SIMD instruction sets. Incidentally, e17 exploited (hardware) GL rendering of 2D graphics via evas a bit before Apple put that into OS X.
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.
Having done a similar work for my final year project this year, I have some experience attempting general purpose computation on a GPU. The results that I recieved when comparing the CPU with the GPU were very different with many of the applications coming in at 7-15 times slower on the GPU. Further, I discovered some problems which I mention below:
! Matrix results
As in mentioned earlier in the report, the graphics pipeline does not support a branch instruction. So with a limitied number of assembly instructions that can be executed in each stage of the pipeline (either 128 or 256 in current cards), how is it possible for them to perform a calculation on a 1500x1500 matrix multiplication. To calculate a single result 1500 multiplications would need to take place and if they are really clever about how they encode the data into texture s to optimise access, they would need two texture accesses for even 4 multiplications. By my calculations that is 1875 instructions, where you can only do 128 or 256.
My tests found that using the Cg compiler provided by NVidia, that a matrix of size 26x26 could be multiplied before the unrolling of the for loop exceed the 256 limitation.
One aspect that my evaluation did not get to examine was the possiblity of reading partial results back from the framebuffer to the texture memory along with loading a slightly modified program to generate the next partial result. They don't mention if they used this strategy so I assume that they don't.
! Inclusion of a branch instruction
Even if a branch instruction were to be included into the vertex and fragment stages of the pipeline, it would cause serious timing issues. As student of Computer Science, I have been taught that the pipeline operates at the speed of the slowest stage and from designing simple pipelined ALUs, I see the logic behind it. However, if a branch instruction is included then the fragment processing stage could become the slowest as the pipeline stalls waiting for the fragment processor to output its information into the framebuffer. I believe it for this reason that the GPU designers specifically did not include a branch instruction.
! Accuracy
My work also found a serious accuracy issue with attempting compuation on the GPU. Firstly, the GPU hardware represents all number in the pipeline as floating point values. As many of you can probably guess, this brings up the ever present problem of 'floating point error'. The interface between GPU and CPU are traditionally 8-bit values. Once they are imported into the 32-bit floating point pipeline the representation has them falling between 0 and 1, meaning that these numbers must be scaled up to their intended representations (integers between 0 and 255 for example) before computation can begin. Combine these two necessary operations and what I saw was a serious accuracy issue where five of my nine results(in the 3x3 matrix) were one integer value out.
While I don't claim to be an expert on these matters, I do think there is the possiblity of using commodity graphics cards for general purpose computation. However, using hardware that is not designed for this purpose holds some serious constraints in my opinion. Anyone who cares to look at my work can find it here