Toward performance models of MPI implementations for understanding application scaling issues

  • Authors:

  • Torsten Hoefler;William Gropp;Rajeev Thakur;Jesper Larsson Träff

  • Affiliations:

  • University of Illinois at Urbana-Champaign, IL;University of Illinois at Urbana-Champaign, IL;Argonne National Laboratory, Argonne, IL;Dept. of Scientific Computing, University of Vienna, Austria

  • Venue:
  • EuroMPI'10 Proceedings of the 17th European MPI users' group meeting conference on Recent advances in the message passing interface
  • Year:
  • 2010

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Abstract

Designing and tuning parallel applications with MPI, particularly at large scale, requires understanding the performance implications of different choices of algorithms and implementation options. Which algorithm is better depends in part on the performance of the different possible communication approaches, which in turn can depend on both the system hardware and the MPI implementation. In the absence of detailed performance models for different MPI implementations, application developers often must select methods and tune codes without the means to realistically estimate the achievable performance and rationally defend their choices. In this paper, we advocate the construction of more useful performance models that take into account limitations on network-injection rates and effective bisection bandwidth. Since collective communication plays a crucial role in enabling scalability, we also provide analytical models for scalability of collective communication algorithms, such as broadcast, allreduce, and all-to-all. We apply these models to an IBM Blue Gene/P system and compare the analytical performance estimates with experimentally measured values.