Preserving Time in Large-Scale Communication Traces
Presenter: Prasun Ratn
Paper link
Abstract
Analyzing the performance of large-scale scientific applications
is becoming increasingly difficult due to the sheer size
of performance data gathered. Recent work on scalable communication
tracing applies online interprocess compression to
address this problem. Yet, analysis of communication traces
requires knowledge about time progression that cannot trivially
be encoded in a scalable manner during compression.
We develop scalable time stamp encoding schemes for communication
traces. At the same time, our work contributes
novel insights into the scalable representation of time stamped
data. We show that our representations capture sufficient information
to enable what-if explorations of architectural variations
and analysis for path-based timing irregularities while
not requiring excessive disk space. We evaluate the ability of
several time-stamped compressed MPI trace approaches to enable
accurate timed replay of communication events. Our lossless
traces are orders of magnitude smaller, if not near constant
size, regardless of the number of nodes while preserving
timing information suitable for application tuning or assessing
requirements of future procurements. Our results prove timepreserving
tracing without loss of communication information
can scale in the number of nodes and time steps, which is a
result without precedent.