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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."
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 questionable. Even worse, it's almost impossible for one research group to compare the "codes" that yielded their results against those produced by another group when numerical computing is used, whereas equations are universally portable.
The theoretical half of the scientific method has lost some of the firm foundations upon which it used to build in recent years, as a result of theorizing through numerical simulation. Fortunately it doesn't matter too much in most sciences because experiment soon demolishes any incorrect predictions. However, those sciences which deal with long-term or historic or otherwise untestable areas are suffering, as a fair bit of unsubstantiated nonsense is popping out of poorly approximated simulations and being claimed as "fact", even though reality hasn't agreed yet.
Things are probably going to get worse in this area before they get better.
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?
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...
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.
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ahh yes the idea of personal supercomputing. Back in '99 I worked for Patmos International. We were at the Linux Expo for that year as well if some of you might remember. Our dream was to have a parallel supercomputer in everyone's home. We used mostly Lisp and Daisy for the programming aspect. The idea was wonderful, but eventually came to a screeching halt when nothing was being sold. It was ahead of it's time for sure. you can find out a little more about it here. I find the whole ideal of symbolic multiprocessing very fascinating though.
*plays the Apogee theme song music*
The problem is how do you actually define supercomputer. I mean, does only machines released in the past month count? Or do you still count the original bad boys like the Cray? After all, when first built most Crays were multi million dollar number crunching beasts. Does the fact that you can get the same performance in a desktop now mean the Cray no longer counts? The power of computers is still growing at such a pace that the machine that costs millions a decade ago can probably be beaten by a cluster that would cost you less than 25K today, so how exactly would you suggest they define supercomputer?
ACs don't waste your time replying, your posts are never seen by me.