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 get thicket from its GitHub repository:
$ git clone https://github.com/llnl/thicket.git
or install it using pip:
$ pip install llnl-thicket
If you are new to thicket and want to start using it, see Getting Started.
If you encounter bugs while using thicket, you can report them by opening an issue on GitHub.
- Tutorial Materials
- Basic Thicket Tutorial: Thicket 101
- Clustering RAJA Performance Suite Dataset: Thicket Tutorial
- Thicket and Extra-P: Thicket Tutorial
- Statistical and Visualization Functions: Thicket Tutorial
- Query Language: Thicket Tutorial
- Thicket Visualization Demonstration