UberCloud HPC Experiment

I think I originally saw a link for the UberCloud HPC Experiment on one of the "This week in CFD" posts on Another Fine Mesh. It is a market research exercise to see how people would use cloud computing (platform as a service, software as a service) for their high performance computing workloads. The focus of the experiment is on the difference between enterprise work-loads and high-performance or scientific computing work-loads. Here's some of the introduction describing the research:

We found that, in particular, small- and medium-sized enterprises in digital manufacturing would strongly benefit from HPC in the Cloud (or HPC as a Service). The major benefits they would realize by having access to additional remote compute resources are: the agility gained by speeding up product design cycles through shorter simulation run times; the superior quality achieved by simulating more sophisticated geometries or physics; and the discovery of the best product design by running many more iterations. These are benefits that increase a company’s competitiveness.

Tangible benefits like these make HPC, and more specifically HPC as a Service, quite attractive. But how far away are we from an ideal HPC cloud model? At this point, we don’t know. However, in the course of this experiment as we followed each team closely and monitored its challenges and progress, we gained an excellent insight into these roadblocks and how our teams have tackled them.
UberCloud HPC Experiment: Compendium of Cases

Each of their teams has an industry user, a resource provider, a software provider, and an HPC expert.

This part on applications is interesting:

By far, computational fluid dynamics (CFD) was the main application run in the cloud by the Round 1 and Round 2 teams – 11 of the 25 teams presented here concentrated their efforts in this area.

I think this really helps make the case for open source CFD codes:

In addition to unpredictable costs associated with pay-per-use billing, incompatible software licensing models are a major headache. Fortunately many of the software vendors, especially those participating in the Experiment, are working on creating more flexible, compatible licensing models, including on-demand licensing in the cloud.

Paying a per-core license makes no sense for these large jobs.

One of the interesting use cases was from Team 30 who used an open source stack (Elmer, CAELinux) on top of Amazon Web Services Elastic Compute Cloud.