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The harmonic or geometric mean: does it really matter?

Published: 01 September 2006 Publication History

Abstract

For several decades, computer scientists have been arguing which mean is more appropriate for summarizing computer performance: the harmonic or the geometric. We show that many test cases used in the past to discredit one mean or the other are either artificial or incidental. Changing only one of the benchmarks may result in totally different conclusions.In addition, we conclude that for the SPEC CPU2000 benchmark suite, the choice of averaging has very little influence on the relative standing of different machines. Therefore, the decision to purchase one system rather then another should not be influenced by the type of averaging used.

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Cited By

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  • (2024)R.I.P. Geomean Speedup Use Equal-Work (Or Equal-Time) Harmonic Mean Speedup InsteadIEEE Computer Architecture Letters10.1109/LCA.2024.336192523:1(78-82)Online publication date: 5-Feb-2024
  • (2023)Hmem: A Holistic Memory Performance Metric for Cloud ComputingBenchmarking, Measuring, and Optimizing10.1007/978-981-97-0316-6_11(171-187)Online publication date: 3-Dec-2023
  • (2023)Python meets JIT compilers: A simple implementation and a comparative evaluationSoftware: Practice and Experience10.1002/spe.326754:2(225-256)Online publication date: 5-Sep-2023
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Published In

cover image ACM SIGARCH Computer Architecture News
ACM SIGARCH Computer Architecture News  Volume 34, Issue 4
September 2006
37 pages
ISSN:0163-5964
DOI:10.1145/1186736
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 2006
Published in SIGARCH Volume 34, Issue 4

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Cited By

View all
  • (2024)R.I.P. Geomean Speedup Use Equal-Work (Or Equal-Time) Harmonic Mean Speedup InsteadIEEE Computer Architecture Letters10.1109/LCA.2024.336192523:1(78-82)Online publication date: 5-Feb-2024
  • (2023)Hmem: A Holistic Memory Performance Metric for Cloud ComputingBenchmarking, Measuring, and Optimizing10.1007/978-981-97-0316-6_11(171-187)Online publication date: 3-Dec-2023
  • (2023)Python meets JIT compilers: A simple implementation and a comparative evaluationSoftware: Practice and Experience10.1002/spe.326754:2(225-256)Online publication date: 5-Sep-2023
  • (2022)RegCPython: A Register-based Python Interpreter for Better PerformanceACM Transactions on Architecture and Code Optimization10.1145/356897320:1(1-25)Online publication date: 16-Dec-2022
  • (2021)Revisiting Issues in Benchmark Metric SelectionPerformance Evaluation and Benchmarking10.1007/978-3-030-84924-5_3(35-47)Online publication date: 4-Aug-2021
  • (2020)Computer comparisons in the presence of performance variationFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-7319-214:1(21-41)Online publication date: 1-Feb-2020
  • (2019)Spread-n-shareProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3295500.3356152(1-15)Online publication date: 17-Nov-2019
  • (2016)Benchmarking deterministic optimization algorithms using an outranking approachOptimization Methods & Software10.1080/10556788.2016.119148431:6(1149-1168)Online publication date: 1-Nov-2016
  • (2015)Many-Core Processors Granularity Evaluation by Considering Performance, Yield, and Lifetime ReliabilityIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2014.235907623:10(2043-2053)Online publication date: 1-Oct-2015
  • (2015)Statistical Performance Comparisons of ComputersIEEE Transactions on Computers10.1109/TC.2014.231561464:5(1442-1455)Online publication date: 1-May-2015
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