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International Conference on Algorithms and Architectures for Parallel Processing

ICA3PP 2015: Algorithms and Architectures for Parallel Processing pp 583–596Cite as

Fast Data-Dependence Profiling by Skipping Repeatedly Executed Memory Operations

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9531))

Abstract

Nowadays, more and more program analysis tools adopt profiling approaches in order to obtain data dependences because of their ability of tracking dynamically allocated memory, pointers, and array indices. However, dependence profiling suffers from high time overhead. To lower the overhead, former dependence profiling techniques either exploit features of the specific program analyses they are designed for, or let the profiling process run in parallel. Although they successfully lowered the time overhead of dependence profiling by a certain amount, none of them have tried to solve the fundamental problem that causes the high time overhead: the memory operations that are repeatedly executed in loops. In most of the time, these memory operations lead to exactly the same data dependences. However, a profiling method has to profile all these memory operations over and over again in order to not miss a single dependence that may occur just once. In this paper, we present a method that allow a dependence profiling technique to skip memory operations that are repeatedly executed in loops without missing any single data dependence. Our method works with all types of loops and does not require any prepossessing like source annotation of the input code. Experiment results show that our method can lower the time overhead of data-dependence profiling by up to 52 %.

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Author information

Authors and Affiliations

  1. Technische Universität Darmstadt, 64289, Darmstadt, Germany

    Zhen Li, Ali Jannesari & Felix Wolf

  2. RWTH Aachen University, 52062, Aachen, Germany

    Michael Beaumont

Authors
  1. Zhen Li
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  2. Michael Beaumont
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  3. Ali Jannesari
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  4. Felix Wolf
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Corresponding author

Correspondence to Zhen Li .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Guojun Wang

  2. The University of Sydney, Sydney, New South Wales, Australia

    Albert Zomaya

  3. University of Murcia, Murcia, Murcia, Spain

    Gregorio Martinez

  4. Hunan University, Changsha, China

    Kenli Li

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© 2015 Springer International Publishing Switzerland

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Li, Z., Beaumont, M., Jannesari, A., Wolf, F. (2015). Fast Data-Dependence Profiling by Skipping Repeatedly Executed Memory Operations. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9531. Springer, Cham. https://doi.org/10.1007/978-3-319-27140-8_40

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  • DOI: https://doi.org/10.1007/978-3-319-27140-8_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27139-2

  • Online ISBN: 978-3-319-27140-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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