Thicket is 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 non-interactive tutorials from this repository of Thicket in the ReadTheDocs.
This repository contains materials for Thicket's hands-on tutorial. You can do all of the exercises on your own laptop using BinderHub.
You find Thicket itself in its Github repository: https://github.com/llnl/thicket
We use BinderHub to create a shareable and interactive environment of the notebooks within a live JupyterHub instance.
You can access the interactive environment at this link or by clicking the badge at the top of this file.
This repository is distributed under the terms of the MIT license.
All contributions must be made under the MIT license. Copyrights are retained by contributors. No copyright assignment is required to contribute to this project.
See LICENSE.
SPDX-License-Identifier: MIT
LLNL-CODE-834749