Thicket
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.
User Docs
If you encounter bugs while using thicket, you can report them by opening an issue on GitHub.
Tutorials
- Tutorial Materials
- Basic Thicket Tutorial: Thicket 101
- HPDC ‘23: Optimization-Based K-means Clustering on the RAJA Performance Suite: Thicket Tutorial
- Thicket and Extra-P: Thicket Tutorial
- Statistical and Visualization Functions: Thicket Tutorial
- Query Language: Thicket Tutorial
- Using Groupby-Aggregate to Compose Multi-Run Datasets: Thicket Tutorial
- Thicket Nsight Compute Reader: Thicket Tutorial
- PPAM ‘24: Composing & Modeling Parallel Sorting Performance Data (Part A): Thicket Tutorial
- PPAM ‘24: Composing & Modeling Parallel Sorting Performance Data (Part B): Thicket Tutorial
- P3HPC ‘24: Top Down and Hierarchical Clustering on the RAJA Performance Suite: Thicket Tutorial
- Thicket Visualization Demonstration
Developer Docs