Ten Things to do with an EC2 GPU

Yesterday I was saying we'll see crypto cracking on EC2 with GPUs. In addition to writing it on this blog, I enthusiastically proposed it to friends who are into this sorta thing and of course we then discovered that someone already did it so we lose all novelty points now and should just go back to thinking about how to make money using cloud based GPUs.

I've been writing about this for five years already and I think EC2 with GPU is like an invitation for me to take my GPU accelerated spreadsheet work into the limelight. A good question is, does Amazon have the capacity to turn that $2.10 per teraflop per hour into $2100 for a Petaflop for an hour? Can they provide it with enough granularity to provide a petaflop for a minute for 2100/60 dollars? I also suspect that some data intensive applications will wish they had solid state disks and faster connections between nodes (as I understand it they have 10Gbps links now). How will we deal with streaming data sets to and from the server? I wonder if you might be reserving the GPUs even when you're not using the GPU, but just moving data to and from the server; this would be a wasteful use of resources.

So here's my list of ten things to do with an EC2 GPU instance.

1) Electronic Design Automation - Giving supercomputer resources to teams of 2 or 3 designers for electronic simulation and synthesis. We definitely want some FPGAs in the cloud for logic simulation and other non-GPU algorithms that FPGAs are good at like pattern recognition. FPGA tools have a way to go and the appliances will need a GPU cloud just to do P&R.

2) Render Farms - NVidia's bought Mental Ray probably knowing they would put Reality Server on EC2. Plugins for Photoshop and Maya are gonna be next; lower resolution screen captures

3) Video Games - A system that renders in the cloud and streams it to your mobile device? Obviously other people are doing this, but access to the Amazon takes the risk out for developers! We can have much much much better game graphics, but controller to screen round trip latency puts an even higher burden to get the computation done faster.

4) Financial Services - Remember when you got to take a 15 minute break after starting a big Correlations or Monte Carlo sim? I wonder if we can get low latency streams of market data and make a sandbox for high frequency traders.

5) Voice Recognition - You know when you're talking to those computer on the phone and it acts like it can recognize you? Not sure how to pipe that volume of data in though.

6) Face Recognition - Soon to be a facebook feature for sure: we can index your face and then auto-tag you.

7) Search for Extra-Terrestrial Intelligence - Actually don't.

8) Computation Molecular Dynamics - Folding on a cloud!

9) Crypto Cracking - If only you had some crypto worth cracking you now have a supercomputer! You could also factor some big numbers, isn't RSA offering money for factoring some big numbers?

10) Verifying Goldbach's Conjecture - We seem to be having trouble proving that every even number is the sum of two primes, but computers keep verifying ever increasingly larger number we throw a them.

$2.10 per teraflop per hour on a $3000 GPU which consumes about 10 cents of power per hour means there's plenty of room for profit and competition in the infrastructure side. I suspect some of the algorithms above can still be designed as services that take advantage of these economics. Unless cloud-based GPUs gets more competitively priced, I suspect anyone selling GPU-accelerated services with constant enough demand would want to use their own server and possibly only tap into cloud resources when they are in need of capacity.