Breadth-First Search on Dynamic Graphs using Dynamic Parallelism on the GPU

Abstract

Breadth-First Search is an important basis for many different graph-based algorithms with applications ranging from peer-to-peer networking to garbage collection. However, the performance of different approaches depends strongly on the type of graph. In this paper, we present an efficient algorithm that performs well on a variety of different graphs. As part of this, we look into utilizing dynamic parallelism in order to both reduce overhead from latency between the CPU and GPU, as well as speed up the algorithm itself. Lastly, integrate the algorithm with the faimGraph framework for dynamic graphs and examine the relative performance to a Compressed-Sparse-Row data structure. We show that our algorithm can be well adapted to the dynamic setting and outperforms another competing dynamic graph framework on our test set.