Yes! Whenever you spin up one of our AMIs, there is a README that will guide you through a couple of simple examples. We are about to publish performance results on the monster machines in a few days, so watch out for it. Scaling depends on the compute density of the GPU workload, but in general we've seen pretty good results with 1) Deep learning (caffe) scaling to 16 GPUs (near native scaling with local GPUs, especially deep nets), 2) Raytracing of photo-realistic and complex scenes - near linear scaling with increasing GPUs, and 3) Physical modeling and simulation does very well too.

Have you done any molecular dynamics benchmarks? If so, what kind/what system? I'd be very interested to see those.

If you haven't, I could probably contribute some strong and weak scaling testcases.

We've only done cursory evaluation of NAMD scaling. We saw a 7X improvement going from a non-GPU system to remote GPUs located in a different datacenter over shared 10g. We're not sure if that was with a representative dataset (MD is not our skill set), so if you can help us with a case study we'd be excited to work with you. Please do contact me.

I sent you a message with some more info and my email via the contact form at bitfusion.io