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 email@example.com.
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
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-4.0.3-py3-none-any.whl (136.4 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
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