Scaling Applications to Massively Parallel Machines Using Projections Performance Analysis Tool
Authors:
Laxmikant V. Kale, Gengbin Zheng, Chee Wai Lee, Sameer Kumar
Parallel Programming Laboratory, Department of Computer Science, University
of Illinois at Urbana-Champaign
Future Generation Computer Systems Journal
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 {\em Projections}, the performance visualization/analysis tool associated with Charm++. We will showcase a series of optimizations facilitated by Projections. The resultant performance of NAMD led to a Gordon Bell award at SC2002 with unprecedented speedup on 3,000 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.