Predictive performance and scalability modeling of a large-scale application

  • Authors:

  • D. J. Kerbyson;H. J. Alme;A. Hoisie;F. Petrini;H. J. Wasserman;M. Gittings

  • Affiliations:

  • Algorithms and Informatics Group, Los Alamos NM;Algorithms and Informatics Group, Los Alamos NM;Algorithms and Informatics Group, Los Alamos NM;Algorithms and Informatics Group, Los Alamos NM;Algorithms and Informatics Group, Los Alamos NM;SAIC and Los Alamos National Laboratory

  • Venue:
  • Proceedings of the 2001 ACM/IEEE conference on Supercomputing
  • Year:
  • 2001

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Abstract

In this work we present a predictive analytical model that encompasses the performance and scaling characteristics of an important ASCI application. SAGE (SAIC's Adaptive Grid Eulerian hydrocode) is a multidimensional hydrodynamics code with adaptive mesh refinement. The model is validated against measurements on several systems including ASCI Blue Mountain, ASCI White, and a Compaq Alphaserver ES45 system showing high accuracy. It is parametric --- basic machine performance numbers (latency, MFLOPS rate, bandwidth) and application characteristics (problem size, decomposition method, etc.) serve as input. The model is applied to add insight into the performance of current systems, to reveal bottlenecks, and to illustrate where tuning efforts can be effective. We also use the model to predict performance on future systems.