SC Conference - Activity Details

Todd Gamblin  (University of North Carolina at Chapel Hill)

Bronis R. de Supinski  (Lawrence Livermore National Laboratory)

Martin Schulz  (Lawrence Livermore National Laboratory)

Rob Fowler  (University of North Carolina at Chapel Hill)

Daniel A. Reed  (Microsoft Research)

Performance Tools

Thursday,  01:30PM - 02:00PM

Room Ballroom E

Good load balance is crucial on very large parallel systems, but the most sophisticated algorithms introduce dynamic imbalances through adaptation in domain decomposition or use of adaptive solvers. To observe and diagnose imbalance, developers need system-wide, temporally-ordered measurements from full-scale runs. This potentially requires data collection from multiple code regions on all processors over the entire execution. Doing this instrumentation naively can, in combination with the application itself, exceed available I/O bandwidth and storage capacity, and can induce severe behavioral perturbations. We present and evaluate a novel technique for scalable, low-error load balance measurement. This uses a parallel wavelet transform and other parallel encoding methods. We show that our technique collects and reconstructs system-wide measurements with low error. Compression time scales sublinearly with system size and data volume is several orders of magnitude smaller than the raw data. The overhead is low enough for online use in a production environment.

The full paper can be found in the IEEE Xplore Digital Library and ACM Digital Library