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A GPU-Based Backtracking Algorithm for Permutation Combinatorial Problems

  • Tiago Carneiro PessoaEmail author
  • Jan Gmys
  • Nouredine Melab
  • Francisco Heron de Carvalho Junior
  • Daniel Tuyttens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10048)

Abstract

This work presents a GPU-based backtracking algorithm for permutation combinatorial problems based on the Integer-Vector-Matrix (IVM) data structure. IVM is a data structure dedicated to permutation combinatorial optimization problems. In this algorithm, the load balancing is performed without intervention of the CPU, inside a work stealing phase invoked after each node expansion phase. The proposed work stealing approach uses a virtual n-dimensional hypercube topology and a triggering mechanism to reduce the overhead incurred by dynamic load balancing. We have implemented this new algorithm for solving instances of the Asymmetric Travelling Salesman Problem by implicit enumeration, a scenario where the cost of node evaluation is low, compared to the overall search procedure. Experimental results show that the dynamically load balanced IVM-algorithm reaches speed-ups up to 17\(\times \) over a serial implementation using a bitset-data structure and up to 2\(\times \) over its GPU counterpart.

Keywords

GPU computing Backtracking Depth-first search Load balancing Work stealing 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Tiago Carneiro Pessoa
    • 1
    Email author
  • Jan Gmys
    • 2
    • 3
  • Nouredine Melab
    • 3
  • Francisco Heron de Carvalho Junior
    • 1
  • Daniel Tuyttens
    • 2
  1. 1.ParGO Research Group (Parallelism, Optimization and Graphs), Mestrado e Doutorado em Ciência da ComputaçãoUniversidade Federal do CearáFortalezaBrazil
  2. 2.Mathematics and Operational Research Department (MARO)University of MonsMonsBelgium
  3. 3.INRIA Lille Nord EuropeUniversité Lille 1, CNRS/CRIStAL, Cité scientifiqueVilleneuve D’AscqFrance

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