A fast work-efficient SSSP algorithm for GPUs

This paper presents a new Single Source Shortest Path (SSSP) algorithm for GPUs. Our key advancement is an improved work scheduler, which is central to the performance of SSSP algorithms. Previous GPU solutions for SSSP use simple work schedulers that can be implemented efficiently on GPUs but that produce low quality schedules. Such solutions yield poor work efficiency and can underutilize the hardware due to a lack of parallelism. Our solution introduces a more sophisticated work scheduler-based on a novel highly parallel approximate priority queue-that produces high quality schedules while being efficiently implementable on GPUs.

To evaluate our solution, we use 226 graph inputs from the Lonestar 4.0 benchmark suite and the SuiteSparse Matrix Collection, and we find that our solution outperforms the previous state-of-the-art solution by an average of 2.9×, showing that an efficient work scheduling mechanism can be implemented on GPUs without sacrificing schedule quality.

While this paper focuses on the SSSP problem, it has broader implications for the use of GPUs, illustrating that seemingly ill-suited data structures, such as priority queues, can be efficiently implemented for GPUs if we use the proper software structure.