Multi-Fidelity Surrogate Modeling for
Application/Architecture Co-Design
Author/Presenters
Event Type
Workshop
Accelerators
Benchmarks
Compiler Analysis and Optimization
Deep Learning
Effective Application of HPC
Energy
Exascale
GPU
I/O
Parallel Application Frameworks
Parallel Programming Languages, Libraries, Models
and Notations
Performance
Simulation
Storage
TimeMonday, November 13th2:30pm -
3pm
Location704-706
DescriptionThe HPC community has been using abstract,
representative applications and architecture models to
enable faster co-design cycles. While developers often
qualitatively verify the correlation of the app
abstractions to the parent application, it is equally
important to quantify this correlation to understand how
the co-design results translate to the parent
application. In this paper, we propose a multi-fidelity
surrogate (MFS) approach which combines data samples of
low-fidelity (LF) models (representative apps and
architecture simulation) with a few samples of a
high-fidelity (HF) model (parent app). The application
of MFS is demonstrated using a multi-physics simulation
application and its proxy-app, skeleton-app, and
simulation models. Our results show that RMSE between
predictions of MFS and the baseline HF models was 4%,
which is significantly better than using either LF or HF
data alone, demonstrating that MFS is a promising
approach for predicting the parent application
performance while staying within a computational
budget.
