Performance Prediction of Multigrid-Solver Configurations

  • Alexander Grebhahn
  • Norbert Siegmund
  • Harald Köstler
  • Sven Apel
Conference paper

DOI: 10.1007/978-3-319-40528-5_4

Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 113)
Cite this paper as:
Grebhahn A., Siegmund N., Köstler H., Apel S. (2016) Performance Prediction of Multigrid-Solver Configurations. In: Bungartz HJ., Neumann P., Nagel W. (eds) Software for Exascale Computing - SPPEXA 2013-2015. Lecture Notes in Computational Science and Engineering, vol 113. Springer, Cham

Abstract

Geometric multigrid solvers are among the most efficient methods for solving partial differential equations. To optimize performance, developers have to select an appropriate combination of algorithms for the hardware and problem at hand. Since a manual configuration of a multigrid solver is tedious and does not scale for a large number of different hardware platforms, we have been developing a code generator that automatically generates a multigrid-solver configuration tailored to a given problem. However, identifying a performance-optimal solver configuration is typically a non-trivial task, because there is a large number of configuration options from which developers can choose. As a solution, we present a machine-learning approach that allows developers to make predictions of the performance of solver configurations, based on quantifying the influence of individual configuration options and interactions between them. As our preliminary results on three configurable multigrid solvers were encouraging, we focus on a larger, non-tivial case-study in this work. Furthermore, we discuss and demonstrate how to integrate domain knowledge in our machine-learning approach to improve accuracy and scalability and to explore how the performance models we learn can help developers and domain experts in understanding their system.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alexander Grebhahn
    • 1
  • Norbert Siegmund
    • 1
  • Harald Köstler
    • 2
  • Sven Apel
    • 1
  1. 1.University of PassauPassauGermany
  2. 2.Friedrich-Alexander University Erlangen-NürnbergErlangenGermany

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