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NVIDIA's $10K Tesla GPU-Based Personal Supercomputer
Posted by
timothy
on Sun Nov 23, 2008 04:25 AM
from the plugs-into-standard-power-strip dept.
from the plugs-into-standard-power-strip dept.
gupg writes "NVIDIA announced a new category of supercomputers — the Tesla Personal Supercomputer — a 4 TeraFLOPS desktop for under $10,000. This desktop machine has 4 of the Tesla C1060 computing processors. These GPUs have no graphics out and are used only for computing. Each Tesla GPU has 240 cores and delivers about 1 TeraFLOPS single precision and about 80 GigaFLOPS double-precision floating point performance. The CPU + GPU is programmed using C with added keywords using a parallel programming model called CUDA. The CUDA C compiler/development toolchain is free to download. There are tons of applications ported to CUDA including Mathematica, LabView, ANSYS Mechanical, and tons of scientific codes from molecular dynamics, quantum chemistry, and electromagnetics; they're listed on CUDA Zone."
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Graphics (Score:5, Funny)
Wow, that's some serious computing power! I wonder if anyone has thought of using these for graphics or rendering? I imagine they could make some killer games, especially with advanced technology like Direct 3D.
Re:Graphics (Score:5, Funny)
Whoosh. Sorry.
Parent
Re:Graphics (Score:5, Funny)
We need a "+1 Whoosh" moderation option.
No, I do not mean "-1 Whoosh". I want to see those embarrassingly stupid postings. But perhaps this moderation option should subtract karma.
Parent
Re:Graphics (Score:5, Funny)
Parent
Re: (Score:3, Informative)
In much the same way that the current Quadro FX cards are based on the same chip as the gaming gforce cards. But still the most expensive gaming card is ~£400, but you'll pay ~£1500 for the top of the line FX5700.
It's because workstation graphics cards are configured for accuracy above all else, where as gaming cards are configured for speed. Having a few pixels being wrong does not affect gaming at all, getting the numbers wrong in simulations is going to cause problems.
Mostly the people who us
Heartening... (Score:3, Interesting)
...to see a company established in a certain market, to branch out so aggressively and boldly into something... well, completely new, really.
Does anyone know if Comsol Multiphysics can be ported to CUDA?
Re:Heartening... (Score:5, Interesting)
Yes, I can. My first thought when I saw the article was to calculate how many of them one would need to simulate a human brain in real time. The answer is: with 2500 of these machines one could simulate a hundred billion neurons with a thousand synapses each, firing a hundred times per second, which is the approximate capacity of a human brain.
People have paid $20 million to visit the space station, now who will be the first millionaire hobbyist to pay $25 million to have his own simulated human brain?
Parent
Re: (Score:3, Interesting)
Would the interconnects be fast enough? There's a lot of non-locality in the synaptic connections, so you're going to need some pretty heavy comms between the cores.
Also a selection of neurons are far more heavily connected than 1000s of synapses, and they're fairly essential ones. Might these be a critical path?
Sure would be cool to build such a beast, do some random connections, and see what happens...
Re: (Score:3, Interesting)
I think your post was intended humorously, but I'm going to pretend otherwise. (Note, I'm not a specialist in computational mentalistics, or whatever the field would be called, but:)
I'm fairly certain the interconnects are fast enough. The brain is no speed demon on individual connections. It's basically chemical, with only a little electrical stuff on top that's still based on ions floating in liquid.
The problem is the software. And the sensoria. And the effectors.
Each of those problems is being addre
Re:Heartening... (Score:5, Interesting)
Your figures are off by several orders of magnitude. 2500 of these is roughly 10,000T/flops. As a Tflop is 10^12 operations, and we have 10^11 neurons that leaves 10^5 floating point operations per neuron. If each has 1000 synapses to process then we are down to 100 operations per connection, per second.
At this point it seems obvious that you've assumed a really simplistic model of a neuron that can compute a synaptic value in a single floating point operation. These simple neuron models don't behave like a real brain, and scaling up simulations of them doesn't produce anything interesting. Real neurons are capable of computing much more complex functions than these models. The throughput on the interconnect is going to be a major factor, and simulating each neuron will require from 10s to 1000000s of operations depending on the level of biological realism that is required. The Blue Brain project has a lot of interesting material on different models of the neuron and the tradeoff between performance and realism.
Their end goal is to dedicate a large IBM Blue Gene to simulating an entire column within the brain (roughly 1,000,000 neurons) using a biologically-realistic model.
Parent
Re:Heartening... (Score:5, Informative)
You're right unless there's a computational way to take advantage of the fact that most neurons in cortex pretty much never fire (1), and that a small minority of synapses are responsible for nearly all of the excitation in a slab of cortical tissue (2). If not active == not important == not necessary to simulate with a 100% duty cycle (these are big "ifs"), then we could be literally about 3-5 orders of magnitude closer to being able to simulate whole brains than anyone realizes.
(1) How silent is the brain: is there a "dark matter" problem in neuroscience? Shy Shoham, Daniel H. O'Connor, Ronen Segev. J Comp Physiol A (2006)
(2) Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits. Sen Song, Per Jesper Sjostro, Markus Reigl, Sacha Nelson, Dmitri B. Chklovskii. PLOS biology March 2005
Parent
4 TFLOPS? (Score:5, Insightful)
A single Radeon 4870x2 is 2.4 TFLOPS. Some supercomputer, that.
Seriously, why is this even news? nVidia makes a product, which is OK, but nothing revolutionary. The devaluation of the "supercomputer" term is appalling.
Also, how much of that 4 TFLOPS you can get on actual applications? How's FFT? Or LINPACK?
Re:4 TFLOPS? (Score:5, Informative)
A single Radeon 4870x2 is 2.4 TFLOPS.
A single Radeon 4870x2 uses two chips. This Tesla thing uses 4 chips that are comparable to the Radeon ones. It should be obvious that they would be in a similar ballpark.
Seriously, why is this even news?
It isn't. Tesla was released a while ago, this is just a slashvertisement.
Parent
It's news because... (Score:3, Interesting)
So, while in theory you could put together some Radeon's, work with their API and achieve the same thing, NVIDIA has significantly reduced the level of effort to make it happen.
Re:4 TFLOPS? (Score:4, Interesting)
Parent
What, no coil? (Score:5, Funny)
Re:What, no coil? (Score:4, Funny)
What a rip.
Yeah, no shit. First bastard that tries to put a "Tesla Capable" sticker on the front, I'm gonna sue.
Parent
What a disappointment (Score:2, Interesting)
Turns out "Tesla" is just the name of the product.
Drat. I demand a refund.
Your probably right about the "mad scientist" ... (Score:3, Insightful)
. . . that's probably exactly the person who would buy one of these.
Folks who are professionally working on mainstream problems that require supercomputers, well, they probably have access to one already. (Maybe one of the supercomputing folks might want to chime in here; do you have enough access/time? Would a baby-supercomputer be useful to you?)
But there is certainly someone out there who was denied access, because his idea was rejected by peer review. He is considered a loopy nut bag, because he
Binary-only toolchain (Score:5, Informative)
The toolchain is binary only and has an EULA that prohibits reverse engineering.
Re:Binary-only toolchain (Score:5, Informative)
has an EULA that prohibits reverse engineering.
Not really a big deal to those of us in the EU since we have a legally guaranteed right to reverse engineer stuff for interoperability purposes.
Parent
Let me be the first to say... (Score:5, Funny)
4 Terraflops should be more than enough for anybody...
4 Terraflops? (Score:3, Funny)
weak DP performance (Score:5, Informative)
boring apps... let's have some realtime raytracing (Score:4, Insightful)
Developement Platform (Score:3, Insightful)
On that note, it would be a good development platform for realtime raytraced game engines. That way the code would be mature when affordable GPU's come out that can match that level of performance.
Erlang (Score:3, Interesting)
Re: (Score:3, Insightful)
By writing an Erlang-to-CUDA compiler?
More seriously though, it is probably not worth even trying, since the GPUs used in the Tesla support a very limited model of parallelism. Shoehorning the flexibility of Erlang into that would at the very leas result in a dramatic performance loss, if it is possible at all.
And in other news... (Score:5, Funny)
... AMD has annouced today it new Edison Personal Supercomputer technology.
The game is on.
cold hard facts about cuda (Score:3, Interesting)
Re:cold hard facts about cuda- unbalanced (Score:5, Insightful)
Parent
Patmos International (Score:3, Interesting)
Re: (Score:2, Interesting)
Port john the ripper/aircrack-ng? Buy a few terabyte drives and start generating hash tables?
FTFL (Score:3, Informative)
All you need to do is follow the fscking link [nvidia.com]. Plenty of examples there.
Re: (Score:3, Interesting)
Neural nets.
This setup sounds ideal for a training bed for fann programs. I can't recall if there's a port of fann for CUDA, but I think there might be.
Re: (Score:3, Funny)
Oh crap, I forgot to click Post AC.
Re: (Score:2, Funny)
Not yet.... darn NVidia, no Vista Drivers yet...
Come on NVidia GET WITH IT!!!
Re: (Score:3, Insightful)
CUDA memory structure (Score:3, Informative)
but I don't know enough about it to be able to give useful information on the subject.
I do write some CUDA code, so I'll try to help.
I believe that each of the chips has a 512 bit wide bus to 4GiB of memory.
Indeed each physical package has entirely access to its own whole chuck of memory, regardless of who many "cores" the package contains (between 2 for the lowest end laptops GPUs and 16 for the highest end 8/9800 cards. Don't know about GT280. But the summary is wrong 240 is probably the amount of ALUs or the width of the SIMD) and regaless of how many "stream processor" there are (each core has 8 ALUs, which are exposed as 32-wide SIMD processing units, which i
Yes but (Score:3, Funny)
And then there is the whole "ECONOMY" thing.
The whole reason the ECONOMY is in the tank is because there are not enough people like you taking loans out against their house to buy random stuff like this.
Basically... IT'S ALL YOUR FAULT!
Re: (Score:3, Interesting)
It's cultural.
You're not even allowed to say that you're "coding", but only that you produce "codes".
Maybe it's because analytic science is basic on equations which become algorithms in computing, and you can't say that you're "equationing" nor "algorithming".
In practice it's actually dishonest, because the algorithms don't have the conceptual power of the equations that they represent (they would if programmed in LISP, but "codes" are mostly written in Fortran and C), so the computations are often question
Weird options (Score:4, Insightful)
I went to the site and tried to configure one. The disk partition options are: "General Purpose, Internet Server, Developer's Workstation, File Server". I wonder, who needs three Tesla cards in a file server or an internet server?
Parent
It also runs Python (Score:4, Informative)
Look, there's Python here [nvidia.com]. You can do the low-level high-performance core routines in C, and use Python to do all the OO programming. This is how God intended us to program.
Parent
Re:It also runs Python (Score:4, Funny)
This is how God intended us to program.
Then why did he write Perl?
Parent
Re:Only in C? Oh dear. (Score:5, Informative)
OO is very good for graphical interfaces, but it isn't particularly well suited for algorithms and other maths oriented stuff.
The term OO is too general to make a statement about its usefulness for mathematics oriented problems. The powerful templating features of modern C++ are indeed very useful for numerical simulations:
It's called C++ Expression Templates, an excellent tool for numerical simulations. ETs can get you very close to the performance of hand optimized C code while they're much more comfortable to use than plain C. Parallelization is also relatively easy to achieve with expression templates.
A research team at my university actually uses expression templates to build some sort of meta compiler which translates C++ ETs into CUDA code. They use it to numerically simulate laser diodes.
Search for papers by David Vandevoorde & Todd Veldhuizen if you want to know more about this. They both developed the technique independently.
Vandevoorde also explains ETs to some degree in his excellent book "C++ Templates - The Complete Guide".
Parent
Re: (Score:3, Informative)
Actually yes it is. For instance nobody has yet figured out an efficient matrix class in C++ that uses operator overloading. This is basically an impossible task to write B=A*X*A^t efficiently, which occurs all the time in linear analysis, because in C++ the transpose would require a copy operator, whereas one ought to get the job done simply with a different iterator. C++ is not equipped for this yet.
Re: (Score:3, Informative)
Re:FLOPS not FLOP! (Score:5, Funny)
Parent
Re:Can I have a smaller version? (Score:4, Informative)
From NVidia's CUDA site, most of their regular display cards support CUDA, just with less cores (hence less performance) than the Tesla card. The cores that CUDA uses are what used to be called the vertex shaders on your (NVidia) card. The CUDA API is designed so that your code doesn't know/specify how many cores are going to be used - you just code to the CUDA architecture and at runtime it distrubutes the workload to the available cores... so you can develop for a low end card (or they even have an emulator) then later pay for th hardware/performance you need.
Parent
Re: (Score:3, Informative)
The 10K refers to a rack mount solution containing 4xGPUs. You can still buy a single GPU and try and put it in a standard machine (provided it doesn't melt - I'd read the specs) for about a quarter of the price.