Comprehensive Evaluation of a New GPU-based Approach to the Shortest Path Problem

The single-source shortest path (SSSP) problem arises in many different fields. In this paper, we present a GPU SSSP algorithm implementation. Our work significantly speeds up the computation of the SSSP, not only with respect to a CPU-based version, but also to other state-of-the-art GPU implementations based on Dijkstra. Both GPU implementations have been evaluated using the latest NVIDIA architectures. The graphs chosen as input sets vary in nature, size, and fan-out degree, in order to evaluate the behavior of the algorithms for different data classes. Additionally, we have enhanced our GPU algorithm implementation using two optimization techniques: The use of a proper choice of threadblock size; and the modification of the GPU L1 cache memory state of NVIDIA devices. These optimizations lead to performance improvements of up to 23