[timeu]

What you wrote about super computer vs cluster is quite right. Recently I attended a HPC meeting where we were the only DevOps of an HPC for a biological institute and most of the other people were from physics & chemistry. They usually don't consider the biology workloads as High Performance Computing but as big resource/data computing. The physics & chemistry guys run simulation using hundred thousands of cores and are mostly CPU bound. They use MPI and their nodes typically have not more than 64 GB and they consider 120 GB memory usage as a lot. Biologist on the other hand hardly use MPI because they can just parallelize the workload on the data level (i.e. sample or chromosome) and run them independently on each node. For that reason also high memory NUMA machines from SGI can relatively often be found.

You are also right that some of the comp bio areas (CryoEM, protein folding, molecular dynamics) are well suited for GPUs