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ReBench: Execute and Document Benchmarks Reproducibly

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ReBench is a tool to run and document benchmark experiments. Currently, it is mostly used for benchmarking language implementations, but it can be used to monitor the performance of all kinds of other applications and programs, too.

The ReBench configuration format is a text format based on YAML. A configuration file defines how to build and execute a set of experiments, i.e. benchmarks. It describes which executable was used, which parameters were given to the benchmarks, and the number of iterations to be used to obtain statistically reliable results.

With this approach, the configuration contains all benchmark-specific information to reproduce a benchmark run. However, it does not capture the whole system.

The data of all benchmark runs is recorded in a data file for later analysis. Important for long-running experiments, benchmarks can be aborted and continued at a later time.

ReBench focuses on the execution aspect and does not provide advanced analysis facilities itself. Instead, the recorded results should be processed by dedicated tools such as scripts for statistical analysis in R, Python, etc, or Codespeed, for continuous performance tracking.

The documentation for ReBench is hosted at

Goals and Features

ReBench is designed to

  • enable reproduction of experiments;
  • document all benchmark parameters;
  • provide a flexible execution model, with support for interrupting and continuing benchmarking;
  • enable the definition of complex sets of comparisons and their flexible execution;
  • report results to continuous performance monitoring systems, e.g., Codespeed;
  • provide basic support for building/compiling benchmarks/experiments on demand;
  • be extensible to parse output of custom benchmark harnesses.


ReBench isn't

  • a framework for microbenchmarks. Instead, it relies on existing harnesses and can be extended to parse their output.
  • a performance analysis tool. It is meant to execute experiments and record the corresponding measurements.
  • a data analysis tool. It provides only a bare minimum of statistics, but has an easily parseable data format that can be processed, e.g., with R.

Installation and Usage

ReBench is implemented in Python and can be installed via pip:

pip install rebench

A minimal configuration file looks like this:

# this run definition will be chosen if no parameters are given to rebench
default_experiment: all
default_data_file: ''

# a set of suites with different benchmarks and possibly different settings
        gauge_adapter: RebenchLog
        command: Harness %(benchmark)s %(input)s %(variable)s
        input_sizes: [2, 10]
            - val1
            - Bench1
            - Bench2

# a set of executables for the benchmark execution
        path: bin
        executable: %(cores)s
        cores: [1]
        path: bin

# combining benchmark suites and executions
          - ExampleSuite
            - MyBin1
            - MyBin2

Saved as test.conf, this configuration could be executed with ReBench as follows:

rebench test.conf

See the documentation for details:

Support and Contributions

In case you encounter issues, please feel free to open an issue so that we can help.

For contributions, we use the normal Github flow of pull requests, discussion, and revisions. For larger contributions, it is likely useful to discuss them upfront in an issue first.

Use in Academia

If you use ReBench for research and in academic publications, please consider citing it.

The preferred citation is:

  author = {Marr, Stefan},
  doi = {10.5281/zenodo.1311762},
  month = {August},
  note = {Version 1.0},
  publisher = {GitHub},
  title = {ReBench: Execute and Document Benchmarks Reproducibly},
  year = 2018

Some publications that have been using ReBench include: