Poster: High Performance GPU Accelerated TSP Solver

Abstract

We are presenting a high performance GPU accelerated implementation of 2-opt local search algorithm for the Traveling Salesman Problem (TSP). GPU usage greatly decreases the time needed to optimize the route, however requires a complicated and well tuned implementation. With the increasing problem size, the time spent on comparing the graph edges grows significantly. We used instances from the TSPLIB library for for testing and our results show that by using our GPU algorithm, the time needed to perform a simple local search operation can be decreased approximately 5 to 45 times compared to parallel CPU code implementation using 6 cores. The code has been implemented in CUDA as well as in OpenCL and tested on NVIDIA and AMD devices. The experimental studies have shown that the optimization algorithm using the GPU local search converges from up to 300 times faster on average compared to the sequential CPU version, depending on the problem size. The main contributions of this work 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.