SC Conference - Activity Details
Space-Efficient Time-Series Call-Path Profiling of Parallel Applications
Authors:| Zoltán Szebenyi (Juelich Supercomputing Centre) |
| Felix Wolf (Juelich Supercomputing Centre) |
| Brian J. N. Wylie (Juelich Supercomputing Centre) |
| Performance Tools |
| Tuesday, 04:30PM - 05:00PM |
| Room PB252 |
The performance behavior of parallel simulations often changes
considerably as the simulation progresses with potentially
process-dependent variations of temporal patterns. While call-path
profiling is an established method of linking a performance problem
to the context in which it occurs, call paths reveal only little
information about the temporal evolution of performance phenomena.
However, generating call-path profiles separately for thousands of
iterations may exceed available buffer space --- especially when
the call tree is large and more than one metric is collected. In
this paper, we present a runtime approach for the semantic
compression of call-path profiles based on incremental clustering of
a series of single-iteration profiles that scales in terms of the
number of iterations without sacrificing important performance
details. Our approach offers low runtime overhead by using only a
condensed version of the profile data when calculating distances and
accounts for process-dependent variations by making all clustering
decisions locally.
The full paper can be found in the
ACM Digital Library and IEEE Computer Society
   |