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Separable 2D Convolution with Polymorphic Register Files

  • Cătălin B. Ciobanu
  • Georgi N. Gaydadjiev
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7767)

Abstract

This paper studies the performance of separable 2D convolution on multi-lane Polymorphic Register Files (PRFs). We present a matrix transposition algorithm optimized for PRFs, and a 2D vectorized convolution algorithm which avoids strided memory accesses. We compare the throughput of our PRF to the nVidia Tesla C2050 GPU. The results show that even in bandwidth constrained systems, multi-lane PRFs can outperform the GPU for 9 ×9 or larger mask sizes.

Keywords

Graphic Processing Unit Single Instruction Multiple Data Polymorphic Register Mask Size General Purpose Processor 
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

  • Cătălin B. Ciobanu
    • 1
    • 2
  • Georgi N. Gaydadjiev
    • 1
    • 2
  1. 1.Computer Engineering Laboratory, EEMCSDelft University of TechnologyThe Netherlands
  2. 2.Department of Computer Science and EngineeringChalmers University of TechnologySweden

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