There is a lot of talk about exascale computing these days, and whether we will be able to reach that goal by 2018, 2019 or whatever.
I have what is probably a naive question. What are the issues with doing it now?
Specifically, today we have the AMD Radeon 295x2
It has a computing power of 11.5 TFLOPS. Combining a hundred thousand of them would give us 1.15 EFLOPS.
The power consumption of each card is slightly under 500 W, so the total consumption of all of them would be 50 MW (there would probably be some more for cooling etc). I'm only guessing, but lets say all the other stuff (cooling and whatever else) took 20 MW. Lets say the price of electric power is 60$/MWh, that would amount to slightly more than 35 million \$ per year. Price of a single graphics card is 1500 \$, which means the hardware would cost 150 million \$. Lets say infrastructure costs another 50 million \$.
Compare this to current fastest supercomputer Tianhe-2. It cost 390 million \$ to make, uses 17.6 MW (24 MW with cooling) and has a processing power of 33.86 PFLOPS.
390 million \$
AMD Radeon 295x2 x 100000
200 million \$
So for the cost of the Tianhe-2, you could build a computer that is more than 30 times faster and have its running costs covered for more than 5 years. I guess that after 5 years supercomputer mostly become obsolete anyway, so you would build another one :)
What am I missing here?
Is there a difference between the floating point operation that are done by today's supercomputers and these GPUs?
Is the problem with AMD not being able to produce/supply 100,000 units of 295x2?
Are there some other practical concerns, like inability to connect 100,000 units into something that would be useful, or inability to cool them properly?
Would AMD Radeons be for some reason unstable or unreliable?