Brief announcement: a GPU accelerated iterated local search TSP solver

  • Authors:

  • Kamil M. Rocki;Reiji Suda

  • Affiliations:

  • University of Tokyo, JST CREST, Tokyo, Japan;University of Tokyo, JST CREST, Tokyo, Japan

  • Venue:
  • Proceedings of the twenty-fourth annual ACM symposium on Parallelism in algorithms and architectures
  • Year:
  • 2012

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Abstract

In this paper we are presenting high performance GPU implementations of the 2-opt and 3-opt local search algorithms used to solve the Traveling Salesman Problem. This type of local search optimization is a very effective and fast method in case of small problem instances. However, the time spent on comparing the graph edges grows significantly with the problem size growing. They are usually a part of global search algorithms such as Iterated Local Search (ILS). Our results showed, that at least 90% of the time during a single ILS run is spent on the local search itself. Therefore we utilized GPU to parallelize the local search and that greatly improved the overall speed of the algorithm. Our results show that the GPU accelerated algorithm finds the optimal swaps approximately 3 to 26 times compared to parallel CPU code using 32 cores, operating at the speed of over 1.5 TFLOPS on a single GeForce GTX 680 GPU. The preliminary experimental studies show that the optimization algorithm using the GPU local search converges 10 to 50 times faster on average compared to the sequential CPU version, depending on the problem size.