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Signed-off-by: Stefan Marr <git@stefan-marr.de>
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smarr committed Jan 3, 2023
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[![PyPI version](https://badge.fury.io/py/ReBench.svg)](https://badge.fury.io/py/ReBench)
[![Documentation](https://readthedocs.org/projects/rebench/badge/?version=latest)](https://rebench.readthedocs.io/)
[![Downloads](https://pepy.tech/badge/rebench)](https://pepy.tech/project/rebench)
[![Codacy Quality](https://api.codacy.com/project/badge/Grade/2f7210b65b414100be03f64fe6702d66)](https://www.codacy.com/app/smarr/ReBench)
[![Coverage](https://coveralls.io/repos/github/smarr/ReBench/badge.svg?branch=master)](https://coveralls.io/github/smarr/ReBench?branch=master)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1311762.svg)](https://doi.org/10.5281/zenodo.1311762)

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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
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
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[Denoise][denoise-docs] configures a Linux system for benchmarking.
It adapts parameters of the CPU frequency management and task scheduling
to reduce some of the variability that can cause widely different
benchmark results for the same experiment.
benchmark results for the same experiment.

Denoise is inspired by [Krun](https://github.com/softdevteam/krun/#readme),
which has many more features to carefully minimize possible interference.
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parameters and suggest them. This includes allowing the execution of `rebench-denoise`
via `sudo` without password.
- [`cpuset`](https://github.com/lpechacek/cpuset/) to reserve cores for benchmarking.
On Ubuntu: `apt install cpuset`
On Ubuntu: `apt install cpuset`

Please note that `rebench-denoise` is only tested on Ubuntu. It is designed to degrade
gracefully and report the expected implications when it cannot adapt system
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- [Tracing vs. Partial Evaluation: Comparing Meta-Compilation Approaches for Self-Optimizing Interpreters](https://stefan-marr.de/papers/oopsla-marr-ducasse-meta-tracing-vs-partial-evaluation/), S. Marr, S. Ducasse. 2015.
- [Zero-Overhead Metaprogramming: Reflection and Metaobject Protocols Fast and without Compromises](https://stefan-marr.de/papers/pldi-marr-et-al-zero-overhead-metaprogramming/), S. Marr et al. 2015.
- [Pycket: a tracing JIT for a functional language](https://www.hpi.uni-potsdam.de/hirschfeld/publications/media/BaumanBolzHirschfeldKirilichevPapeSiekTobinHochstadt_2015_PycketATracingJitForAFunctionalLanguage_AcmDL.pdf), S. Bauman et al. 2015.
- [Adaptive just-in-time value class optimization: transparent data structure inlining for fast execution](https://hpi.uni-potsdam.de/hirschfeld/publications/media/PapeBolzHirschfeld_2015_AdaptiveJustInTimeValueClassOptimization_AcmDL.pdf), T. Pape et al. 2015.
- [Adaptive just-in-time value class optimization: transparent data structure inlining for fast execution](https://hpi.uni-potsdam.de/hirschfeld/publications/media/PapeBolzHirschfeld_2015_AdaptiveJustInTimeValueClassOptimization_AcmDL.pdf), T. Pape et al. 2015.
- [Meta-tracing makes a fast Racket](https://www.hpi.uni-potsdam.de/hirschfeld/publications/media/BolzPapeSiekTobinHochstadt_2014_MetaTracingMakesAFastRacket_AuthorsVersion.pdf), C. F. Bolz et al. 2014.
- [Cloud PARTE: Elastic Complex Event Processing based on Mobile Actors](https://stefan-marr.de/downloads/agere13-swalens-et-al-cloud-parte-elastic-complex-event-processing-based-on-mobile-actors.pdf), J. Swalens et al. 2013.
- [Identifying A Unifying Mechanism for the Implementation of Concurrency Abstractions on Multi-Language Virtual Machines](https://www.stefan-marr.de/downloads/tools12-smarr-dhondt-identifying-a-unifying-mechanism-for-the-implementation-of-concurrency-abstractions-on-multi-language-virtual-machines.pdf), S. Marr, T. D'Hondt. 2012.
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