A Python-based toolkit for Exploratory Data Analysis (EDA) of parallel performance data that enables performance optimization and understanding of applications’ performance on supercomputers. It bridges the performance tool gap between being able to consider only a single instance of a simulation run (e.g., single platform, single measurement tool, or single scale) and finding actionable insights in multi-dimensional, multi-scale, multi-architecture, and multi-tool performance datasets. You can find detailed documentation, along with tutorials of Thicket in the ReadtheDocs.
To use thicket, install it with pip:
$ pip install llnl-thicket
Or, if you want to develop with this repo directly, run the install script from the
root directory, which will build the package and add the cloned directory to
your PYTHONPATH:
$ source install.sh
You can direct any feature requests or questions to the Lawrence Livermore National Lab's Thicket development team by emailing either Stephanie Brink (brink2@llnl.gov) or Olga Pearce (pearce8@llnl.gov).
To contribute to Thicket, please open a pull request to the develop branch. Your pull request must pass Thicket's unit tests, and must be PEP 8 compliant. Please open issues for questions, feature requests, or bug reports.
Many thanks to Thicket's contributors.
Thicket was created by Olga Pearce and Stephanie Brink.
To cite Thicket, please use the following citation:
- Stephanie Brink, Michael McKinsey, David Boehme, Connor Scully-Allison, Ian Lumsden, Daryl Hawkins, Treece Burgess, Vanessa Lama, Jakob Lüttgau, Katherine E. Isaacs, Michela Taufer, and Olga Pearce. 2023. Thicket: Seeing the Performance Experiment Forest for the Individual Run Trees. In the 32nd International Symposium on High-Performance Parallel and Distributed Computing (HPDC'23), August 2023, Pages 281–293. doi.org/10.1145/3588195.3592989.
On GitHub, you can copy this citation in APA or BibTeX format via the "Cite this repository" button. Or, see CITATION.cff for the raw BibTeX.
Thicket is distributed under the terms of the MIT license.
All contributions must be made under the MIT license. Copyrights in the Thicket project are retained by contributors. No copyright assignment is required to contribute to Thicket.
See LICENSE and NOTICE for details.
SPDX-License-Identifier: MIT
LLNL-CODE-834749