Scalable and Efficient Linear Algebra Kernel Mapping for Low Energy Consumption on the Layers CGRA

  • Zoltán Endre Rákossy
  • Dominik Stengele
  • Axel Acosta-Aponte
  • Saumitra Chafekar
  • Paolo Bientinesi
  • Anupam Chattopadhyay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9040)

Abstract

A scalable mapping is proposed for 3 important kernels from the Numerical Linear Algebra domain, to exploit architectural features to reach asymptotically optimal efficiency and a low energy consumption. Performance and power evaluations were done with input data set matrix sizes ranging from 64\(\times \)64 to 16384\(\times \)16384. 12 architectural variants with up to 10\(\times \)10 processing elements were used to explore scalability of the mapping and the architecture, achieving \(<10\,\%\) energy increase for architectures up to 8\(\times \)8 PEs coupled with performance speed-ups of more than an order of magnitude. This enables a clean area-performance trade-off on the Layers architecture while keeping energy constant over the variants.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zoltán Endre Rákossy
    • 1
  • Dominik Stengele
    • 2
  • Axel Acosta-Aponte
    • 1
  • Saumitra Chafekar
    • 1
  • Paolo Bientinesi
    • 2
  • Anupam Chattopadhyay
    • 3
  1. 1.Institute for Communication Technologies and Embedded Systems (ICE)AachenGermany
  2. 2.Algorithmically-Driven Code Generation for High-Performance, Computing Architectures, AICESRWTH Aachen UniversityAachenGermany
  3. 3.School of Computer EngineeringNanyang Technological UniversityNanyangSingapore

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