XBFS: eXploring Runtime Optimizations for Breadth-First Search on GPUs

TL;DR: XBFS is proposed that leverages the runtime optimizations atop GPUs to cope with the nondeterministic characteristics of BFS with the following three techniques: first, XBFS adaptively exploits four either new or optimized frontier queue generation designs to accommodate various BFS levels that present dissimilar features.

Abstract: Attracted by the enormous potentials of Graphics Processing Units (GPUs), an array of efforts has surged to deploy Breadth-First Search (BFS) on GPUs, which, however, often exploits the static mechanisms to address the challenges that are dynamic in nature. Such a mismatch prevents us from achieving the optimal performance for offloading graph traversal on GPUs. To this end, we propose XBFS that leverages the runtime optimizations atop GPUs to cope with the nondeterministic characteristics of BFS with the following three techniques: First, XBFS adaptively exploits four either new or optimized frontier queue generation designs to accommodate various BFS levels that present dissimilar features. Second, inspired by the observation that the workload associated with each vertex is not proportional to its degree in bottom-up, we design three new strategies to better balance the workload. Third, XBFS introduces the first truly asynchronous bottom-up traversal which allows BFS to visit vertices for multiple levels at a single iteration with both theoretical soundness and practical benefits. Taken together, XBFS is, on average, 3.5×, 4.9×, 11.2× and 6.1× faster than the state-of-the-art Enterprise, Tigr, Gunrock on a Quadro P6000 GPU and Ligra on a 24-core Intel Xeon Platinum 8175M CPU. Note, the CPU used for Ligra is more expensive than the GPU for XBFS.