Performance Modeling under Resource Constraints Using
Deep Transfer Learning
Authors
Event Type
Paper
Machine Learning
Performance
Quantum Computing
TimeWednesday, November 15th1:30pm -
2pm
Location301-302-303
DescriptionTuning application parameters for optimal performance
is a combinatorially challenging problem. Hence,
techniques for modeling the functional relationships
between various input features in the parameter space
and application performance are important. We show that
simple statistical inference techniques are inadequate
to capture these relationships, and that even with more
complex ensembles of models, the minimum coverage of the
parameter space required via experimental observations
is still quite large. We propose a deep learning-based
approach that can combine the knowledge from exhaustive
observations collected at a smaller scale with limited
observations collected at a larger target scale. The
proposed approach is able to accurately predict
performance in the regimes of interest to performance
analysts, while outperforming many traditional
techniques. In particular, our approach can identify the
best performing configurations when trained using as
little as 1% of observations at the target scale.
Download PDF:
here
Authors
