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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."
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?
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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