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Optimizing Program Performance via Similarity, Using a Feature-Agnostic Approach

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Advanced Parallel Processing Technologies (APPT 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8299))

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Abstract

This work proposes a new technique for performance evaluation to predict performance of parallel programs across diverse and complex systems. In this work the term system is comprehensive of the hardware organization, the development and execution environment.

The proposed technique considers the collection of completion times for some pairs (program, system) and constructs an empirical model that learns to predict performance of unknown pairs (program, system). This approach is feature-agnostic because it does not involve previous knowledge of program and/or system characteristics (features) to predict performance.

Experimental results conducted with a large number of serial and parallel benchmark suites, including SPEC CPU2006, SPEC OMP2012, and systems show that the proposed technique is equally applicable to be employed in several compelling performance evaluation studies, including characterization, comparison and tuning of hardware configurations, compilers, run-time environments or any combination thereof.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of California Irvine, Irvine, USA

    Rosario Cammarota, Laleh Aghababaie Beni, Alexandru Nicolau & Alexander V. Veidenbaum

Authors
  1. Rosario Cammarota
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  2. Laleh Aghababaie Beni
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  3. Alexandru Nicolau
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  4. Alexander V. Veidenbaum
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Editor information

Editors and Affiliations

  1. Institute of Computing Technology, State Key Laboratory of Computer Architecture, Chinese Academy of Sciences, No. 6 Kexueyuan South road, Haifian District, 100190, Beijing, China

    Chenggang Wu

  2. Département d’Informatique, INRIA and École Normale Supérieure, 45 rue d’Ulm, 75005, Paris, France

    Albert Cohen

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Cammarota, R., Beni, L.A., Nicolau, A., Veidenbaum, A.V. (2013). Optimizing Program Performance via Similarity, Using a Feature-Agnostic Approach. In: Wu, C., Cohen, A. (eds) Advanced Parallel Processing Technologies. APPT 2013. Lecture Notes in Computer Science, vol 8299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45293-2_15

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  • DOI: https://doi.org/10.1007/978-3-642-45293-2_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45292-5

  • Online ISBN: 978-3-642-45293-2

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