Extra-P, automated performance modeling for HPC applications
Automated performance modeling for HPC applications
Extra-P is an automatic performance-modeling tool that supports the user in the identification of scalability bugs. A scalability bug is a part of the program whose scaling behavior is unintentionally poor, that is, much worse than expected. A performance model is a formula that expresses a performance metric of interest such as execution time or energy consumption as a function of one or more execution parameters such as the size of the input problem or the number of processors.
Extra-P uses measurements of various performance metrics at different execution configurations as input to generate performance models of code regions (including their calling context) as a function of the execution parameters. All it takes to search for scalability issues even in full-blown codes is to run a manageable number of small-scale performance experiments, launch Extra-P, and compare the asymptotic or extrapolated performance of the worst instances to the expectations.
Extra-P generates not only a list of potential scalability bugs but also human-readable models for all performance metrics available such as floating-point operations or bytes sent by MPI calls that can be further analyzed and compared to identify the root causes of scalability issues.
For questions regarding Extra-P please send a message to firstname.lastname@example.org.
Table of Contents
- Python 3.7 or higher
- PySide2 (for GUI)
- matplotlib (for GUI)
- pyobjc-framework-Cocoa (only for GUI on macOS)
Use the following command to install Extra-P and all required packages via
python -m pip install extrap --upgrade
--upgrade forces the installation of a new version if a previous version is already installed.
Extra-P can be used in two ways, either using the command-line interface or the graphical user interface. More information about the usage of Extra-P with both interfaces can be found in the quick start guide.
Graphical user interface
The graphical user interface can be started by executing the
Command line interface
The command line interface is available under the
extrap OPTIONS (
You can use different input formats as shown in the examples below:
- Text files:
extrap --text test/data/text/one_parameter_1.txt
- JSON files:
extrap --json test/data/json/input_1.JSON
- Talpas files:
extrap --talpas test/data/talpas/talpas_1.txt
- Create model and save it to text file at the given path:
extrap --out test.txt --text test/data/text/one_parameter_1.txt
The Extra-P command line interface has the following options.
|FILEPATH||Specify a file path for Extra-P to work with|
||Show help message and exit|
||Show program's version number and exit|
||Set program's log level (default:
||Load data from CUBE files|
||Load data from text files|
||Load data from Talpas data format|
||Load data from JSON or JSON Lines file|
||Load data from Extra-P 3 experiment|
||Set weak or strong scaling when loading data from CUBE files (default:
||Use median values for computation instead of mean values|
||Selects the modeler for generating the performance models|
||Options for the selected modeler|
||Show help for modeler options and exit|
||Specify the output path for Extra-P results|
||Set which information should be displayed after modeling (default:
||Saves the experiment including all models as Extra-P experiment (if no extension is specified, “.extra-p” is appended)|
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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|Filename, size extrap-4.0.2-py3-none-any.whl (135.3 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
|Filename, size extrap-4.0.2.tar.gz (91.6 kB)||File type Source||Python version None||Upload date||Hashes View|