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Perun: Lightweight Performance Version System

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Perun is an open source light-weight Performance Version System, which works as a wrapper over existing Version Control Systems and in parallel manages performance profiles corresponding to different versions of projects. Moreover, it offers a tool suite suitable for automation of the performance regression test runs, postprocessing of existing profiles or effective interpretation of the results.

In particular, Perun has the following advantages over using databases or sole Version Control Systems for the same purpose:

  1. Preserves Context---each performance profile is assigned to a concrete minor version adding the functional context (i.e. code changes) of profiles.

  2. Provides Automation---Perun allows one to easily automate the process of profile collection, eventually reducing the whole process to a single command. The specification of jobs is read from YAML files.

  3. Is Highly Generic---supported format of the performance profiles is based on JSON. Perun tool suite contains a basic set of visualizations, postprocessing and collection modules, but it is easily extensible.

  4. Is Easy to use---the workflow, interface and storage of Perun is heavily inspired by the git systems aiming at natural use.

Perun is intented to be used in two ways: (1) for a single developer (or a small team) as a complete solution for automating, storing and interpreting performance of project or (2) as a dedicated store for a bigger projects and teams. Its git-like design aims at easy distribution and simple interface makes it a good store of profiles along with the context.


You can install Perun as follows:

git clone
cd perun
make init
make install

These commands install Perun to your system as a runnable python package. You can then run Perun safely from the command line using the perun command. Run either perun --help or see the cli documentation for more information about running Perun commands from command line. Note that depending on your OS and the location of Python libraries, you might require root permissions to install Perun.

It is advised to verify that Perun is running correctly in your environment as follows:

make test

Installing Tracer Dependencies

The standard Perun installation does not automatically install the instrumentation frameworks used by Tracer: SystemTap and eBPF. Installing these frameworks is optional when using Perun, although having at least one of them is required in order to run Tracer. Moreover, both frameworks rely on system-wide packages and thus should be installed directly by the user when needed.

SystemTap: Ubuntu

SystemTap can be installed using apt-get:

sudo apt-get install systemtap

Furthermore, kernel debug symbols package must be installed in order to use SystemTap. For Ubuntu 16.04 and higher:

sudo apt-key adv --keyserver --recv-keys C8CAB6595FDFF622 
codename=$(lsb_release -c | awk  '{print $2}')
sudo tee /etc/apt/sources.list.d/ddebs.list << EOF
deb ${codename}      main restricted universe multiverse
deb ${codename}-security main restricted universe multiverse
deb ${codename}-updates  main restricted universe multiverse
deb ${codename}-proposed main restricted universe multiverse

sudo apt-get update
sudo apt-get install linux-image-$(uname -r)-dbgsym

To test that SystemTap works correctly, run the following command:

stap -v -e 'probe {printf("read performed\n"); exit()}'

For more information, see the source.

SystemTap: Fedora

SystemTap can be installed using yum:

sudo yum install systemtap systemtap-runtime

Similarly to the Ubuntu case, additional kernel packages must be installed to run SystemTap properly:


Different Fedora versions use different methods for obtaining those packages. Please refer to the SystemTap setup guide

BCC: Ubuntu

Perun uses the BCC (BPF Compiler Collection) frontend for the eBPF technology. We recommend obtaining the necessary packages from the IO Visor repository:

sudo apt-key adv --keyserver --recv-keys 4052245BD4284CDD
echo "deb$(lsb_release -cs) $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/iovisor.list
sudo apt-get update
sudo apt-get install bcc-tools libbcc-examples linux-headers-$(uname -r)

The default BCC installation uses bindings for Python 2, however, Perun requires bindings for Python 3. To install them, run the following command:

sudo apt-get install python3-bcc

BCC: Fedora

Installing BCC on Fedora is much easier. Simply run:

sudo dnf install bcc python3-bcc

BCC: python virtualenv

Note that when using Perun in a Python virtual environment, the above installation instructions are not enough. Since the Python bcc package is not available through pip, installing it directly in a virtualenv using pip requirements list is not an option. A common workaround is to copy the system-wide python bcc package installed in the previous step (python3-bcc) into the virtualenv packages.

To find the system python3 bcc package, run:

python3 -c "import site; print(site.getsitepackages())"

which shows the potential global site-packages paths (not all of the paths must necessarily exist). The package bcc should be located in at least one the listed path (otherwise the installation of python3-bcc must have failed in the previous step). The resulting path may look like e.g.:


Run the same command with an activated virtualenv to get the list of site-packages paths local to the virtualenv. As with the global site-packages path, not all of the listed paths must exist - usually only one truly does. E.g.:


Next, copy the bcc package from the global site-packages to the virtualenv local site-packages:

cp -r /usr/lib/python3/dist-packages/bcc <prefix>/venv-3.8/lib/python3.8/site-packages/

Now the bcc package should be available in the virtualenv python. Test the following command with activated virtualenv:

python3 -c "import bcc"

which should successfully finish (i.e. ModuleNotFoundError should not be raised).


Alternatively you can install Perun in development mode:

git clone
cd perun
make init
make dev

This method of installation allows you to make a changes to the code, which will be then reflected by the installation.

Again, it is advised to verify that Perun is running correctly in your environment as follows:

make test

If you are interested in contributing to Perun project, please refer to contributing section. If you think your results could help others, please send us PR, we will review the code and in case it is suitable for wider audience, we will include it in our upstream.

But, please be understanding, we cannot fix and merge everything.

Getting Started

In order to start managing performance of your project tracked by git, go to its directory and run the following:

perun init --vcs-type=git --configure

This creates a parallel directory structure for Perun storage (stored in .perun), and runs the initial configuration of the local project settings in text editor (by default vim). There you can chose the set of collectors, postprocessors and specify which commands (and with which configurations) should be profiled. See configuration for more details about perun's configuration.

Now start collecting the profiles for current version of your project:

perun run matrix

This command collects set of profiles, according to the previously set configuration (see specification of job matrix for more details). You can then view the list of collected and registered profiles, and visualize the profiles (see visualization overview), or check for possible performance changes (see degradation documentation):

# Show list of profiles
perun status

# Show the first generated profile using scatter plot
perun show 0@p scatter -v

# Register the first generated profile to current minor version
perun add 0@p

Now anytime one can do code changes, commit them, rerun the collection phase, register new profiles and check whether any change in performance can be detected:

# Rerun the collection
perun run matrix

# Register the profiles for current minor version
perun add 0@p

# Run the degradation check
perun check head


In the following, we list the foremost features and advantages of Perun:

  • Unified format---we base our format of performance profiles on JSON.

  • Natural specification of Profiling Runs---we base the specification of profiling jobs in Yaml format.

  • Git-inspired Interface---the cli is inspired by git version control systems and specifies commands like e.g. add, remove, status, or log.

  • Efficient storage---performance profiles are stored compressed in the storage in parallel to versions of the profiled project inspired by git.

  • Multiplatform-support---Perun is implemented in Python 3 and its implementation is supported both by Windows and Unix-like platforms.

  • Regression Analysis---Perun's suite contains a postprocessing module for regression analysis of profiles (see regression analysis documentation), which supports several different strategies for finding the best predicting model for given data (such as linear, quadratic, or constant model).

  • Interactive Visualizations---Perun's tool suite includes several visualization modules, some of them based on Bokeh visualization library, which provides nice and interactive plots, in exchange of scalability.

  • Useful API for profile manipulation---helper modules are provided for working with our profiles in external applications ---we have API for executing simple queries over the resources or other parts of the profiles, or convert and transform the profiles to different representations (e.g. pandas data frame). See conversion api and query api for overview.

  • Automatic Detection of Performance Degradation---we are currently exploring effective heuristics for automatic detection of performance degradation between two project versions (e.g. between two commits).

As a sneak peek, we are currently working and exploring the following featurues in near future of the project:

  • Regular Expression Driven Collector---collector based on parsing the standard text output for a custom specified metrics, specified by regular expressions.

  • Fuzzing Collector---collector based on method of fuzz testing ---i.e. modifying inputs in order to force error or, in our case, a performance change.

  • Clustering Postprocessor---we are exploring now how to make any profile usable for regression analysis.

  • Automatic Hooks---the automatic hooks, that will allow to automate the runs of job matrix, automatic detection of degradation and efficient storage.

For more information about Perun's feature, please refer to our extensive list of features!


If you'd like to contribute, please fork the repository and use a feature branch. Pull requests are warmly welcome.

In case you run in some unexpected behaviour, error or anything suspicious, either contact us directly through mail or create a new Issue.

The architecture of Perun allows easy extension. In case you are interested in extending our tool suite with new kinds of collectors, postprocessors or visualization methods, please refer to appropriate sections in Perun's documentation (i.e. Create your own collector, postprocessor or visualization).

If you are interested in contributing to Perun project, please first refer to contributing section. If you think your custom module could help others, please send us PR, we will review the code and in case it is suitable for wider audience, we will include it in our upstream.

But, please be understanding, we cannot fix and merge everything.


Unrelated links:


The code in this project is licensed under GNU GPLv3 license.


We thank for the support received from Red Hat (especially branch of Brno), and Brno University of Technology (BUT FIT).

Further we would like to thank the following individuals (in the alphabetic order) for their (sometimes even just a little) contributions:

  • Jan Fiedor (Honeywell)---for feedback, and technical discussions;
  • Jirka Hladky and his team (RedHat)---for technical discussions;
  • Martin Hruska (BUT FIT)---for feedback, and technical discussions;
  • Petr Müller (SAP)---for nice discussion about the project;
  • Michal Kotoun (BUT FIT)---for feedback, and having faith in this repo;
  • Hanka Pluhackova (BUT FIT)---for awesome logo, theoretical discussions about statistics, feedback, and lots of ideas;
  • Adam Rogalewicz (BUT FIT)---for support, theoretical discussions, feedback;
  • Tomas Vojnar (BUT FIT)---for support, theoretical discussions, feedback;
  • Jan Zeleny (Red Hat)---for awesome support, and feedback.

Development of this tool has been supported by AQUAS project (Aggregated Quality Assurance for Systems, This project has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 737475. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, France, United Kingdom, Austria, Italy, Czech Republic, Germany.

This tool as well as the information provided on this web page reflects only the author's view and ECSEL JU is not responsible for any use that may be made of the information it contains.

This project is co-funded by the European Union