Scaling applications to massively parallel machines using Projections performance analysis tool

Some of the most challenging applications to parallelize scalably are the ones that present a relatively small amount of computation per iteration. Multiple interacting performance challenges must be identified and solved to attain high parallel efficiency in such cases. We present case studies involving NAMD, a parallel classic molecular dynamics application for large biomolecular systems, and CPAIMD, Car-Parrinello ab initio molecular dynamics application, and efforts to scale them to large number of processors. Both applications are implemented in Charm++, and the performance analysis was carried out using Projections, the performance visualization/analysis tool associated with Charm++. We showcase a series of optimizations facilitated by Projections. The resultant performance of NAMD led to a Gordon Bell award at SC 2002 with unprecedented speedup on 3000 processors with teraflops level peak performance. We also explore the techniques for applying the performance visualization/analysis tool on future generation extreme-scale parallel machines and discuss the scalability issues with Projections.