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Integrating Critical-Blame Analysis for Heterogeneous Applications into the Score-P Workflow

In: Tools for High Performance Computing 2014

Author

Listed:
  • Felix Schmitt

    (Technische Universität Dresden, Center for Information Services and High Performance Computing)

  • Robert Dietrich

    (Technische Universität Dresden, Center for Information Services and High Performance Computing)

  • Jonas Stolle

    (Technische Universität Dresden, Center for Information Services and High Performance Computing)

Abstract

High performance computing (HPC) systems increasingly deploy accelerators and coprocessors to achieve maximum performance combined with high energy efficiency. Thus, application design for such large-scale heterogeneous clusters often requires to utilize multiple programming models that scale both within and across nodes and accelerators. To assist programmers in the complex task of application development and optimization, sophisticated performance analysis tools are necessary. It has been shown that CASITA, an analysis tool for complex MPI, OpenMP and CUDA applications, is able to effectively identify valuable optimization targets by means of critical-blame analysis for applications utilizing multiple programming models. This paper presents the integration of CASITA into the Score-P tool infrastructure. We depict the complete Score-P measurement and analysis workflow, including the performance data collection for the CUDA, OpenMP and MPI programming models, tracking of dependencies between work performed on the host and on the accelerator as well as waiting-time and critical-blame analysis with CASITA and visualization of analysis results in Vampir.

Suggested Citation

  • Felix Schmitt & Robert Dietrich & Jonas Stolle, 2015. "Integrating Critical-Blame Analysis for Heterogeneous Applications into the Score-P Workflow," Springer Books, in: Christoph Niethammer & José Gracia & Andreas Knüpfer & Michael M. Resch & Wolfgang E. Nagel (ed.), Tools for High Performance Computing 2014, edition 127, pages 161-173, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-16012-2_8
    DOI: 10.1007/978-3-319-16012-2_8
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