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  • Benchmarking as a method of assessing software performance is known to suffer from random fluctuations that distort the observed performance. In this paper, we focus on the fluctuations caused by compilation. We show that the design of a benchmarking experiment must reflect the existence of the fluctuations if the performance observed during the experiment is to be representative of reality. We present a new statistical model of a benchmark experiment that reflects the presence of the fluctuations in compilation, execution and measurement. The model describes the observed performance and makes it possible to calculate the optimum dimensions of the experiment that yield the best precision within a given amount of time. Using a variety of benchmarks, we evaluate the model within the context of regression benchmarking. We show that the model significantly decreases the number of erroneously detected performance changes in regression benchmarking.
  • Benchmarking as a method of assessing software performance is known to suffer from random fluctuations that distort the observed performance. In this paper, we focus on the fluctuations caused by compilation. We show that the design of a benchmarking experiment must reflect the existence of the fluctuations if the performance observed during the experiment is to be representative of reality. We present a new statistical model of a benchmark experiment that reflects the presence of the fluctuations in compilation, execution and measurement. The model describes the observed performance and makes it possible to calculate the optimum dimensions of the experiment that yield the best precision within a given amount of time. Using a variety of benchmarks, we evaluate the model within the context of regression benchmarking. We show that the model significantly decreases the number of erroneously detected performance changes in regression benchmarking. (en)
Title
  • Precise Regression Benchmarking with Random Effects: Improving Mono Benchmark Results
  • Precise Regression Benchmarking with Random Effects: Improving Mono Benchmark Results (en)
skos:prefLabel
  • Precise Regression Benchmarking with Random Effects: Improving Mono Benchmark Results
  • Precise Regression Benchmarking with Random Effects: Improving Mono Benchmark Results (en)
skos:notation
  • RIV/00216208:11320/06:00206184!RIV10-GA0-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET400300504), P(GD201/05/H014), Z(MSM0021620838)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 493923
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/06:00206184
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Precise; Regression; Benchmarking; Random; Effects; Improving; Benchmark; Results (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [9D797A33F30E]
http://linked.open...v/mistoKonaniAkce
  • Neuveden
http://linked.open...i/riv/nazevZdroje
  • Third European Performance Engineering Workshop, EPEW 2006, Budapest, Hungary, June 21-22, 2006
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Kalibera, Tomáš
  • Tůma, Petr
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000239480100005
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Stochastic Modeling Laboratory, Dept. of Telecommunications, Technical University of Budapest
https://schema.org/isbn
  • 3-540-35362-3
http://localhost/t...ganizacniJednotka
  • 11320
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