Scaling Applications to Massively Parallel Machines Using Projections Performance Analysis Tool
Future Generation Computer Systems Journal (FGCS) 2004
Publication Type: Paper
Repository URL: mdPerfStudy04
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.
Laxmikant V. Kale and Gengbin Zheng and Chee Wai Lee and Sameer Kumar, "Scaling Applications to Massively Parallel Machines Using Projections Performance Analysis Tool", Future Generation Computer Systems Special Issue on: Large-Scale System Performance Modeling and Analysis, vol. 22, pp. 347-358, Febuary, 2006.
Research Areas