Applied Parallel and Scientific Computing

Volume 7134 of the series Lecture Notes in Computer Science pp 463-473

Further Improving the Scalability of the Scalasca Toolset

  • Markus GeimerAffiliated withJülich Supercomputing Centre
  • , Pavel SaviankouAffiliated withJülich Supercomputing Centre
  • , Alexandre StrubeAffiliated withJülich Supercomputing CentreUniversitat Autònoma de Barcelona
  • , Zoltán SzebenyiAffiliated withJülich Supercomputing CentreRWTH Aachen University
  • , Felix WolfAffiliated withJülich Supercomputing CentreRWTH Aachen UniversityGerman Research School for Simulation Sciences
  • , Brian J. N. WylieAffiliated withJülich Supercomputing Centre

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Scalasca is an open-source toolset that can be used to analyze the performance behavior of parallel applications and to identify opportunities for optimization. Target applications include simulation codes from science and engineering based on the parallel programming interfaces MPI and/or OpenMP. Scalasca, which has been specifically designed for use on large-scale machines such as IBM Blue Gene and Cray XT, integrates runtime summaries suitable to obtain a performance overview with in-depth studies of concurrent behavior via event tracing. Although Scalasca was already successfully used with codes running with 294,912 cores on a 72-rack Blue Gene/P system, the current software design shows scalability limitations that adversely affect user experience and that will present a serious obstacle on the way to mastering larger scales in the future. In this paper, we outline how to address the two most important ones, namely the unification of local identifiers at measurement finalization as well as collating and displaying analysis reports.


Scalasca scalability