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Complications for Computational Experiments from Modern Processors

Authors Johannes K. Fichte , Markus Hecher , Ciaran McCreesh, Anas Shahab

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Author Details

Johannes K. Fichte
  • TU Dresden, Germany
Markus Hecher
  • TU Wien, Austria
  • Universität Potsdam, Germany
Ciaran McCreesh
  • University of Glasgow, UK
Anas Shahab
  • TU Dresden, Germany

Funding

  • Fichte, Johannes K.: Google Fellowship at the Simons Institute, UC Berkeley.
  • Hecher, Markus: FWF Grants Y698 and P32830 and Grant WWTF ICT19-065.

Acknowledgements

Authors are given in alphabetical order. The work has been carried out while the first three authors were visiting the Simons Institute for the Theory of Computing.

Cite AsGet BibTex

Johannes K. Fichte, Markus Hecher, Ciaran McCreesh, and Anas Shahab. Complications for Computational Experiments from Modern Processors. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 25:1-25:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.CP.2021.25

Abstract

In this paper, we revisit the approach to empirical experiments for combinatorial solvers. We provide a brief survey on tools that can help to make empirical work easier. We illustrate origins of uncertainty in modern hardware and show how strong the influence of certain aspects of modern hardware and its experimental setup can be in an actual experimental evaluation. More specifically, there can be situations where (i) two different researchers run a reasonable-looking experiment comparing the same solvers and come to different conclusions and (ii) one researcher runs the same experiment twice on the same hardware and reaches different conclusions based upon how the hardware is configured and used. We investigate these situations from a hardware perspective. Furthermore, we provide an overview on standard measures, detailed explanations on effects, potential errors, and biased suggestions for useful tools. Alongside the tools, we discuss their feasibility as experiments often run on clusters to which the experimentalist has only limited access. Our work sheds light on a number of benchmarking-related issues which could be considered to be folklore or even myths.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Multicore architectures
  • Hardware → Temperature monitoring
  • Hardware → Impact on the environment
  • Hardware → Platform power issues
  • Theory of computation → Design and analysis of algorithms
Keywords
  • Experimenting
  • Combinatorial Solving
  • Empirical Work

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Supplementary Materials

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