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

Felix Schmitt (), Robert Dietrich () and Jonas Stolle ()
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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

A chapter in Tools for High Performance Computing 2014, 2015, pp 161-173 from Springer

Abstract: 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.

Keywords: Critical Path; High Performance Computing; Event Stream; Wait State; CUDA Kernel (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-16012-2_8

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DOI: 10.1007/978-3-319-16012-2_8

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