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NVIDIA's $10K Tesla GPU-Based Personal Supercomputer
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."
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?
While the inner nerd in me screams to take out a loan against my house to buy one, I can't imagine this being very popular outside academia. Most users don't use the power of their crappy computers, let alone this. And then there is the whole "ECONOMY" thing.
"Chance favors the prepared mind." ~Me
OO is very good for graphical interfaces, but it isn't particularly well suited for algorithms and other maths oriented stuff. Why should we care if OO fanboys are scared off? Decent developers know to use the right tool for the job, not try to shoehorn whatever their personal favourite is into every situation.
there were a lot of early efforts trying to implement realtime rayracing engines for games (e.g. at Intel recently), let's port that stuff and have some fun.
"I love my job, but I hate talking to people like you" (Freddie Mercury)
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?
It would take a hell of a lot more than 25 mil to program the brain simulator.
People are always coming out of the wood work to claim supercomputer performance with such and such a solution, go back and look at GRAPE (which is really cool.) http://arstechnica.com/news.ars/post/20061212-8408.html or a lot of other supercomputer clusters. When you want something flexible, you look for "balance" that means a good relationship between memory capacity, latency & bandwidth, as well as computer power. in terms of memory capacity, the number people talk about is: 1 byte/flop... that is 1 Tbyte of memory is about right to keep 1 TFLOP flexibly useful. this thing has 4 G of memory for 4 TF... in other words: 1 byte / 1000 flops. it's going to be hard to use in a general purpose way.
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.
. . . 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 wants to prove that the Higg's boson is made of cottage cheese, or something like that.
Yep, look for rejected supercomputing program proposals, and you have a list of potential customers.
Schroedinger's Brexit: The UK is both in and out of the EU at the same time!
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.