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High Performance GPU Accelerated Local Optimization in TSP

Kamil Rocki and Reiji Suda
The University of Tokyo
Third Workshop on Parallel Computing and Optimization (PCO’13) in conjunction with 27th IEEE International Parallel & Distributed Processing Symposium (IPDPS), May 20-24, 2013, Boston, USA (to appear)

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This paper presents a high performance GPU accelerated implementation of 2-opt local search algorithm for the Traveling Salesman Problem (TSP). GPU usage significantly decreases the execution time needed for tour optimization, however it also requires a complicated and well tuned implementation. With the problem size growing, the time spent on local optimization comparing the graph edges grows significantly. According to our results based on the instances from the TSPLIB library, the time needed to perform a simple local search operation can be decreased approximately 5 to 45 times compared to a corresponding parallel CPU code implementation using 6 cores. The code has been implemented in OpenCL and as well as in CUDA and tested on AMD and NVIDIA devices. The experimental studies show that the optimization algorithm using the GPU local search converges from up to 300 times faster compared to the sequential CPU version on average, depending on the problem size. The main contributions of this paper are the problem division scheme exploiting data locality which allows to solve arbitrarily big problem instances using GPU and the parallel implementation of the algorithm itself.
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