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Mastering Software Variant Explosion for GPU Accelerators

  • Richard Membarth
  • Frank Hannig
  • Jürgen Teich
  • Mario Körner
  • Wieland Eckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7640)

Abstract

Mapping algorithms in an efficient way to the target hardware poses a challenge for algorithm designers. This is particular true for heterogeneous systems hosting accelerators like graphics cards. While algorithm developers have profound knowledge of the application domain, they often lack detailed insight into the underlying hardware of accelerators in order to exploit the provided processing power. Therefore, this paper introduces a rule-based, domain-specific optimization engine for generating the most appropriate code variant for different Graphics Processing Unit (GPU) accelerators. The optimization engine relies on knowledge fused from the application domain and the target architecture. The optimization engine is embedded into a framework that allows to design imaging algorithms in a Domain-Specific Language (DSL). We show that this allows to have one common description of an algorithm in the DSL and select the optimal target code variant for different GPU accelerators and target languages like CUDA and OpenCL.

Keywords

Graphic Processing Unit Local Operator Local Memory Iteration Space Code Variant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Richard Membarth
    • 1
  • Frank Hannig
    • 1
  • Jürgen Teich
    • 1
  • Mario Körner
    • 2
  • Wieland Eckert
    • 2
  1. 1.Hardware/Software Co-Design, Department of Computer ScienceUniversity of Erlangen-NurembergGermany
  2. 2.Siemens Healthcare Sector, H IM AXForchheimGermany

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