P75: Model-Agnostic Influence Analysis for Performance
Data
SessionPoster Reception
Event Type
ACM Student Research Competition
Poster
Reception
TimeTuesday, November 14th5:15pm -
7pm
LocationFour Seasons Ballroom
DescriptionExecution time of an application is affected by several
performance parameters, e.g. number of threads,
decomposition, etc. Hence, an important problem in high
performance computing is to study the influence of these
parameters on the performance of an application.
Additionally, quantifying the influence of individual
parameter configurations (data samples) on performance
also aids in identifying sub-domains of interest in
high-dimensional parameter spaces. Conventionally, such
analysis is performed using a surrogate model, which
introduces its own bias that is often non-trivial to
undo, leading to inaccurate results. In this work, we
propose an entirely data-driven, model-agnostic
influence analysis approach based on recent advances in
analyzing functions on graphs. We show that the problem
of identifying influential parameters (features) and
configurations (samples) can be effectively addressed
within this framework.
Authors
