A Parallel GPU Version of the Traveling Salesman Problem

This paper describes and evaluates an implementation of iterative hill climbing with random restart for determining high-quality solutions to the traveling salesman problem. With 100,000 restarts, this algorithm finds the optimal solution for four out of five 100-city TSPLIB inputs and yields a tour that is only 0.07% longer than the optimum on the fifth input. The presented implementation is highly parallel and optimized for GPU-based execution. Running on a single GPU, it evaluates over 20 billion tour modifications per second. It takes 32 CPUs with 8 cores each (256 cores total) to match this performance.

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