Lessons Learned from Exploring the Backtracking Paradigm on the GPU

  • John Jenkins
  • Isha Arkatkar
  • John D. Owens
  • Alok Choudhary
  • Nagiza F. Samatova
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6853)

Abstract

We explore the backtracking paradigm with properties seen as sub-optimal for GPU architectures, using as a case study the maximal clique enumeration problem, and find that the presence of these properties limit GPU performance to approximately 1.4–2.25 times a single CPU core. The GPU performance “lessons” we find critical to providing this performance include a coarse-and-fine-grain parallelization of the search space, a low-overhead load-balanced distribution of work, global memory latency hiding through coalescence, saturation, and shared memory utilization, and the use of GPU output buffering as a solution to irregular workloads and a large solution domain. We also find a strong reliance on an efficient global problem structure representation that bounds any efficiencies gained from these lessons, and discuss the meanings of these results to backtracking problems in general.

Keywords

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • John Jenkins
    • 1
    • 2
  • Isha Arkatkar
    • 1
    • 2
  • John D. Owens
    • 3
  • Alok Choudhary
    • 4
  • Nagiza F. Samatova
    • 1
    • 2
  1. 1.North Carolina State UniversityRaleighUSA
  2. 2.Oak Ridge National LaboratoryOak RidgeUSA
  3. 3.University of California, DavisDavisUSA
  4. 4.Northwestern UniversityEvanstonUSA

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