Authors: Thomas Randall, Jaehoon Koo, Brice Videau, Michael Kruse, Xingfu Wu, Paul Hovland, Mary Hall, Rong Ge, Prasanna Balaprakash
This repository is provided for transparency and ease-of-replication for our ICS'23 paper, "Transfer-Learning-Based Autotuning Using Gaussian Copula".
Contact: Thomas Randall ([email protected])
License: BSD 2-Clause License
- Benchmarks: Provides source code and tuning space definitions (problem.py) for all benchmarks. Organized by benchmark suite, then benchmark.
- ConditionalSampling: Greater depth presentation and exploration of mathematics and mechanisms of Conditional Sampling with Gaussian Copulas. Provided as an additional resource referenced by the paper.
- Data: Raw experimental data provided for transparency. Files are organized by benchmark suite, benchmark, then each tuning technique.
- GC_TLA: Our technique as well as all scripts necessary to replicate our experiments and analyses.
- After cloning, run
python3 softLinkDataDirs.pyto establish symbolic links between respective entries of the Benchmarks and Data directories.
Slides and recorded talks may not be uploaded to GitHub, but relevant URLs will be made accessible here.
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. ICS'23, June 21-23, 2023, Orlando, FL, USA © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM ACM ISBN 979-8-4007-0056-9/23/06 https://doi.org/10.1145/3577193.3593712
This research was partially supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration, and by U.S. National Science Foundation under Grant CCF-1942182. This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357.