Abstract
This paper proposes an acceleration method for finding the all-pairs shortest paths (APSPs) using the graphics processing unit (GPU). Our method is based on Harish's iterative algorithm that computes the cost of the single-source shortest path (SSSP) in parallel on the GPU. In addition to this fine-grained parallelism, we exploit the coarse-grained parallelism by using a task parallelisation scheme that associates a task with an SSSP problem. This scheme solves multiple SSSP problems at a time, allowing us to efficiently access graph data by sharing the data between processing elements in the GPU. Furthermore, our fine-and coarse-grained parallelisation leads to a higher parallelism, increasing the efficiency with highly threaded code. As a result, the speedup over the previous SSSP-based implementation ranges from a factor of 2.8 to that of 13, depending on the graph topology. We also show that the overhead of path recording needed after cost computation increases the execution time by 7.7%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of High Performance Computing and Networking
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
