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ASSIST: An FDO Source-to-Source Transformation Tool for HPC Applications

  • Youenn Lebras
  • Andres S. Charif Rubial
  • Romain Dolbeau
  • William JalbyEmail author
Conference paper
First Online:

Abstract

The complexity and the diversity of computer architectures have dramaticaly evolved over the last decade, which makes it impossible to manually optimize codes for all these architectures. In addition, compilers must remain conservative with respect to their optimization choices because of their static cost model. One way to guide them is to use feedback data from data profiling of a representative training dataset (FDO/PGO) for a given application. It then becomes possible, based on that knowledge, to add specific compiler directives and/or flags to enhance performance. Moreover, automatic transformations simplifying portions of the application (e.g. specialization) can be applied. In this paper we present ASSIST, a directive-oriented source-to-source manipulation tool that aims at providing such assistance. The tool is integrated into the MAQAO toolset and takes advantage of all the available static and dynamic profiling data produced by the other tools. It also features a set of code transformations triggered by directives. The combination of both leads to an autotuning process that helps users to keep their code as generic as possible whilst also benefiting from a performance gain related to feedback or user knowledge. We demonstrate how we can build a compiler’s PGO-like tool and compare our first results to the Intel compiler PGO mode.

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Notes

Acknowledgements

We would like to thank Gabriel Staffelbach (CERFACS) for providing our laboratory with the AVBP application, as well as Ghislain Lartigue and Vincent Moureau (CORIA) for providing us with YALES2. This work has been carried out by the Li-PaRAD laboratory, PeXL and the Exascale Computing Research laboratory, with the support of CEA, Intel, UVSQ. Intel granted us dedicted access to a Skylake SP machine on which the experiments were run. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the CEA, Intel, or UVSQ.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Youenn Lebras
    • 1
    • 3
  • Andres S. Charif Rubial
    • 1
    • 2
    • 3
  • Romain Dolbeau
    • 4
  • William Jalby
    • 1
    • 3
    Email author
  1. 1.UVSQ/Exascale Computing ResearchVersaillesFrance
  2. 2.PeXLVersaillesFrance
  3. 3.Exascale Research ComputingVersaillesFrance
  4. 4.AtosBezonsFrance

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