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GPUs To Power Supercomputing's Next Revolution
Posted by
Zonk
on Thu Nov 09, 2006 04:51 PM
from the you-can-use-them-to-play-quake-too dept.
from the you-can-use-them-to-play-quake-too dept.
evanwired writes "Revolution is a word that's often thrown around with little thought in high tech circles, but this one looks real. Wired News has a comprehensive report on computer scientists' efforts to adapt graphics processors for high performance computing. The goal for these NVidia and ATI chips is to tackle non-graphics related number crunching for complex scientific calculations. NVIDIA announced this week along with its new wicked fast GeForce 8800 release the first C-compiler environment for the GPU; Wired reports that ATI is planning to release at least some of its proprietary code to the public domain to spur non-graphics related development of its technology. Meanwhile lab results are showing some amazing comparisons between CPU and GPU performance. Stanford's distributed computing project Folding@Home launched a GPU beta last month that is now publishing data putting donated GPU performance at 20-40 times the efficiency of donated CPU performance."
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Impressive GPU Numbers From Folding@Home 201 comments
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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What makes GPUs so great? (Score:3, Funny)
I thought
Oh yeah
*I'm kidding I'm kidding*
Sweet (Score:5, Interesting)
One more step toward GPU Raytracing. We're already pushing rediculous numbers of polygons, with less and less return for our efforts. The future lies in projects like OpenRT [openrt.de]. With any luck, we'll start being able to blow holes through levels rather than having to run the rat-maze.
Re: (Score:2)
cant [kant] -noun
1. insincere, esp. conventional expressions of enthusiasm for high ideals, goodness, or piety.
2. the private language of the underworld.
3. the phraseology peculiar to a particular class, party, profession, etc.: the cant of the fashion industry.
4. whining or singsong speech, esp. of beggars.
-verb (used without object)
5. to talk hypocritically.
6. to speak in the whining or singsong tone o
So... (Score:5, Informative)
Re: (Score:2)
Re: (Score:3, Insightful)
Not really...
PC's run multiple processes that have unpredictable branching - like network protocol stacks, device drivers and word processors and plug'n'play devices. More CPU cores help to spread the load. For the desktop windows system, 3D functionality was simply a bolt-on to the windows system
What I'd like to see come from this (Score:5, Interesting)
Here we go again... (Score:3, Funny)
wider view... (Score:2)
Acronym (Score:3, Informative)
The next thing you know... (Score:4, Funny)
"Serious" computers won't come with fewer than 4 16x PCI-E slots for hooking in "scientific processing units"...
We used to tell our boss that we were going to do stress-testing when we stayed late to play Q3, this takes that joke to a whole new level.
SIMD for the masses (Score:2)
Re: What makes a GPU so great (Score:5, Informative)
GPUs have dedicated circuitry to do math, math, and more math - and to do it *fast*. In a single cycle, they can perform mathematical computations that take general-purpose CPUs an eternity, in comparison.
Sighted in Massachusetts... (Score:5, Funny)
NVIDIA announced this week along with its new wicked fast GeForce 8800 release the first C-compiler environment for the GPU
"Wicked fast" GPU? And a compiler?
Sounds like a Boston C Party.
Practical results (Score:2, Informative)
8800 and Seymour's machines (Score:4, Interesting)
Another overview article NYTIMES and literature? (Score:5, Informative)
Second. Anyone out there working on books that have examples? Please reply with any good 'how to' sources.
Source: http://www.nytimes.com/2006/11/09/technology/09chi p.html?ref=technology [nytimes.com]
SAN JOSE, Calif., Nov. 8 -- A $90 million supercomputer made for nuclear weapons simulation cannot yet be rivaled by a single PC chip for a serious video gamer. But the gap is closing quickly.
Indeed, a new breed of consumer-oriented graphics chips have roughly the brute computing processing power of the world's fastest computing system of just seven years ago. And the latest advance came Wednesday when the Nvidia Corporation introduced its next-generation processor, capable of more than three trillion mathematical operations per second.
Nvidia and its rival, ATI Technologies, which was recently acquired by the microprocessor maker Advanced Micro Devices, are engaged in a technology race that is rapidly changing the face of computing as the chips -- known as graphical processing units, or G.P.U.'s -- take on more general capabilities.
In recent years, the lead has switched quickly with each new family of chips, and for the moment the new chip, the GeForce 8800, appears to give the performance advantage to Nvidia.
On Wednesday, the company said its processors would be priced at $599 and $449, sold as add-ins for use by video game enthusiasts and for computer users with advanced graphics applications.
Yet both companies have said that the line between such chips and conventional microprocessors is beginning to blur. For example, the new Nvidia chip will handle physics computations that are performed by Sony's Cell microprocessor in the company's forthcoming PlayStation 3 console.
The new Nvidia chip will have 128 processors intended for specific functions, including displaying high-resolution video.
And the next generation of the 8800, scheduled to arrive in about a year, will have "double precision" mathematical capabilities that will make it a more direct competitor to today's supercomputers for many applications.
"I am eagerly looking forward to our next generation," said Andy Keane, general manager of Nvidia's professional products division, a business the company set up recently to aim at commercial high-performance computing applications like geosciences and gene splicing.
The chips made by Nvidia and ATI are shaking up the computing industry and causing a level of excitement among computer designers, who in recent years have complained that the industry seemed to have run out of new ideas for gaining computing speed. ATI and Advanced Micro Devices have said they are working on a chip, likely to emerge in 2008, that would combine the functions of conventional microprocessors and graphics processors.
That convergence was emphasized earlier this year when an annual competition sponsored by Microsoft's research labs to determine the fastest sorting algorithm was won this year by a team that used a G.P.U. instead of a traditional microprocessor. The result is significant, according to Microsoft researchers, because sorting is a basic element of many modern computing operations.
Moreover, while innovation in the world of conventional microprocessors has become more muted and largely confined to adding multiple processors, or "cores," to single chips, G.P.U. technology is continuing to advance rapidly.
"The G.P.U. has this incredible memory bandwidth, and it will continue to double for the foreseeable future," said Jim Gray, manager of Microsoft's eScience group.
Although the comparison has many caveats, both computer scientists and game designers said that Nvidia GeForce 8800 had in some ways moved near the realm for the computing power of the supercomputing world of the last decade.
The fastest of thes
Re: So... (Score:2)
Odd? Not really. The "PC super chip" design is practically the same thing as the "GPU Supercomputer" design. The big difference is
Configurable microchips. (Score:2)
FPGAs are getting quite powerful and are getting a lot cheaper. It definitely won't be as fast as a dedicated ASIC, but if programmed properly, it
8800GTX and HPC (Score:5, Interesting)
The addition of a C compiler, drivers specific to GPGPU applications and available for linux (!) as well as XP/Vista means that this is going to be seeing widespread adoption amongst the HPC crowd. There probably won't be any papers on it published at SC06 in Florida next week, but over the next year there probably will be a veritable torrent of publications (there already is a LOT being done with GPUs). The new architecture really promotes GPGPU apps, and the potential performance/$ especially factoring in the development time which should be significantly less with this toolchain. A couple 8800GTXes in SLI and I could be giving traditional clusters a run for their money when it comes to apps like FFTs etc. I can't wait till someone benchmarks FFT performance using CUDA. If anyone finds such numbers post and let me know!
RE: So.... (Score:2)
It's not unusual at all. CPUs are very general and do certain things very quickly & efficiently. GPUs on the other hand do other things very quickly and efficiently. The type of number crunching that GPUs do is actually well suited to the massively repetitive number crunching done by most of the big super computers [think climatology studies]. Shifting from CPU to GPU architectures just makes sense there.
Currently viable solutions (Score:2, Informative)
It's nice to see the name Acceleware mentioned in the NVIDIA press release, although they are missing from the 'comprehensive' report on wired. It should be noted that they have been delivering High performance computing solutions for a couple of years or so already. I guess now it's out of the bag that NVIDIA's little graphics cards had something to with that.
Anyone know of any other companies that have already been commercializing GPGPU technology?
Reply to #16789087 (Score:4, Insightful)
16789087 [slashdot.org]
I picture this:
Before:
CPU makers: "Hardware's expensive, keep it simple."
GPU makers: "We can specialize the expensive hardware separatly!"
Now:
CPU makers: "Hardware's cheaper and cheaper, lets keep up our profits by making our more inclusive."
GPU makers: "We can specialize the cheap hardware in really really big number-crunch projects!"
btw, why isn't the reply button showing up? I'm too lazy to hand type the address.
GPGPU companies (Score:2, Informative)
It gets even better (Score:2, Funny)
NVIDIA's CUDA Technology Explained (Score:2, Informative)
We go into NVIDIA's "CUDA" (Compute Unified Device Architecture) here [hothardware.com] and it's pretty interesting actually.
sigh.. (Score:5, Funny)
power management (Score:2, Interesting)
(1) Power Management : I want at least 3 settings (lowest power, mid-range and max-performance)
(2) Where's the killer app? I value my electricty more than contributing to folding and SETI.
If they address these, I'm a customer... (I'm a cheap bastard who is fine with integrated 6150 graphics)
Re: (Score:2)
But anyways, there's always scientific computing. Guess what all those supercomputers are used for
And now imagine a GPU-driven Beowulf cluster
Better computers? (Score:2)
Obviously some of that is due to GPUs being better than general-purpose CPUs at this sort of math, but how much is also due to the fact that the people who are willing to run a Beta version of Folding@Home on their GPU tend to be the sort of people who would have much better computers overall than those who are merely running the project on their
But can it *run* Linux? (Score:2)
Re: (Score:2)
hybrids (Score:2, Interesting)
"In a sign of the growing importance of graphics processors, chipmaker Advanced Micro Devices inked a deal in July to acquire ATI for $5.4 billion, and then unveiled plans to develop a new "fusion" chip that combines CPU and GPU functions."
I can see the coming age of multi-core CPU's not necessarily lasting very long now. We don't tend to need a large number of general-purpose CPU's. But a CPU+GPU chip, where the GPU has for example 128 1.35GHz cores (from t
Where are the GPU-assisted encoders? (Score:2)
Re:So... (Score:2)
Umm... No. There's no evidence of that at all.
Nope. Making the GPU just another core on the CPU chip would make PCs more able to utilize the GPU quickly, for these types of tasks.
Workshop and Tutorials at SC'06 (Score:2)
While it's probably too late to sign up for the general-purpose GPU tutorial at Supercomputing '06, there may still be time to get to the "General-Purpose GPU Computing: Practice and Experience" workshop (assuming you're going to Supercomputing to begin with.) Workshop's web page is http://www.gpgpu.org/sc2006/workshop/ [gpgpu.org]
The workshop itself has turned into a kind of "GPU and multi-core" forum, with lots of great speakers. NVIDIA's Ian Buck and ATI's Mark Segal will both be speaking to th
Re:Another overview article NYTIMES and literature (Score:4, Informative)
Ok, ok, here's the link [uni-dortmund.de]...
AVIVO (Score:2)
What about FPGAs (Score:2)
Not symmetric (Score:2)
Intel's 80 core chip wasn't symmetric; most of those cores were stripped-down processors, not x86 standard. Like the Cell, only more so.
nVidia's G80, while not on the same chip, takes this to 128 cores. G90 will support full double-precision math. And although it's separate from the CPU, graphics cards are such a standard part of most systems that by the time five years have elapsed, you'll likely be able to get a quad-core x86 + 256-core DP gfx/HPC system for somewhat less than Intel's fancy new 80-core r
Re: What makes a GPU so great (Score:2, Insightful)
Re:power management (Score:2)
I've read this isn't quite as much a waste of electricity as it seems, at least during the winter if you have electric heating. The majority of the energy consumed by your CPU goes into thermal energy, which your heatsink disipates into the air. Thus every watt your CPU burns is one wat your furnace doesn't have to burn to keep your house warm enough. I'm sure it doesn't work out perfectly, but one way you're running a whole bunch of ele
Re: accuracy problems (Score:4, Informative)
You're confusing your technologies. The RAM used on video cards these days is effectively the same RAM you use with your CPU. The memory cannot lose data or very bad things will happen to the rendering pipeline.
What you're thinking of is the intentional inaccuracy of the floating point calculations done by the GPU. In order to obtain the highest absolute graphical performance, most 3D drivers optimized for gaming attempt to drop the precision of the calculations to a degree that's unacceptable for engineering uses, but perfectly acceptable for gaming. NVidia and ATI make a lot of money by selling "professional" cards like the Quadro and the FireGL to engineering companies that need the greater precision. A lot of the difference is in the drivers (especially for the low-end models), but the cards do often have hardware technologies better suited to CAD-type work.
GPUs good for a FEW supercomputing tasks. (Score:2)
secondly, the bandwidth to memory is very high, but the amount of addressable memory is very very low. 768MB of memory, divided by 128 p
Re: What makes a GPU so great (Score:2)
Its great for highly parallel processor bound applications, but for anything close to user level apps its just a waste of silicon.
8087 (Score:4, Funny)
Sounds like there is a lot of untapped potential. I propose we move GPUs off the external cards, and give them their own dedicated spot on the motherboard. Though, since we will allowing it be used for more general applications, we could just call it a Math Processor. Then again, it's not really a full processor like a duel core, so, we'll just call it a Co-Processor. This new "Math Co- Processor" will revolutionize PCs like nothing we have ever seen before. Think of it, who would have thought 20 years ago we could have a whole chip just for floating point math!
So the Transputer was a good idea after all. (Score:2)
Remember this [wikipedia.org]? although it was a failure commercially, it was the right idea after all: lots of small processing units that are able to process in parallel big chunks of data; that's what modern GPUs do.
So what we need now is for this kind of architecture to pass in CPUs (maybe already scheduled from what I've read lately) and then a programming language where operations are parallel, except when data dependencies exist (functional languages may be good for this task).
what about a Fortran compiler ? (Score:2)
Re: (Score:2)
Re: (Score:2)
This is not english. Even if I try, I can't guess what you are trying to tell. It makes no sense whatsoever...
Please understand what "turing computable" means. All computers are devices capable of emulating a turing machine (at least if it fits within RAM, etc). And computers is something you can emulate on a turing machine. Your criticism is like someone complaining tha