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

• Getting Started
  • Prerequisites
  • Other Packages
  • Installation
• User Guide
  • Thicket Components
  • Performance Data
  • Metadata
  • Aggregated Statistics
• Generating Profiling Datasets
  • Caliper
  • Adiak

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
  • 1. Import Necessary Packages
  • 2. Read in Performance Profiles
  • 3. More Information on a Function
  • 4. Thicket Components
• Clustering RAJA Performance Suite Dataset: Thicket Tutorial
  • 1. Import Necessary Packages
  • 2. Utility Functions for Ingesting and Manipulating Performance Data
  • 3. Utility Functions for KMeans
  • 4. Set Variables to be Used for All Clustering
  • 5. Thicket Clustering
  • 6. Plot the Clusters as Scatterplots
• Thicket and Extra-P: Thicket Tutorial
  • 1. Import Necessary Packages
  • 2. Define Dataset Paths and Names
  • 3. More Information on a Function
  • 3. Create Models
  • 4. Models Dataframe
  • 5. Show the Models Dataframe with Embedded Plots
  • 6. Query Specific Model
  • 7. Operations on a model
• Statistical and Visualization Functions: Thicket Tutorial
  • 1. Import Necessary Packages
  • 2. Read in Performance Profiles
  • 3. More Information on a Function
  • 4. Creating a Combined Thicket
  • 5. Aggregated Statistic Functions
  • 6. Visualization Thicket Functions
• Query Language: Thicket Tutorial
  • 1. Import Necessary Packages
  • 2. Read in Performance Profiles
  • 3. More Information on a Function
  • 4. Append Statistical Calculation(s)
  • 5. Thicket Query Language
• Thicket Visualization Demonstration
  • Top-down Analysis
  • Parallel Coordinates Plot

Developer Docs

• Developer Guide
  • Contributing to Thicket

API Docs

• Thicket API Docs
  • Subpackages
  • Submodules
  • thicket.ensemble module
  • thicket.groupby module
  • thicket.helpers module
  • thicket.model_extrap module
  • thicket.ncu module
  • thicket.thicket module
  • thicket.utils module
  • thicket.version module
  • Module contents

Indices and tables

• Index

• Module Index

• Search Page