Scaling Applications to Massively Parallel Machines Using Projections Performance Analysis Tool
Future Generation Computer Systems Journal (FGCS) 2004
Publication Type: Paper
Repository URL: mdPerfStudy04
Abstract
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.
TextRef
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.
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