Efficient breadth-first search on a heterogeneous processor

Abstract

Accelerating breadth-first search (BFS) can be a compelling value-add given its pervasive deployment. The current state-of-the-art hybrid BFS algorithm selects different traversal directions based on graph properties, thereby, possessing heterogeneous characteristics. Related work has studied this heterogeneous BFS algorithm on homogeneous processors. In recent years heterogeneous processors have become mainstream due to their ability to maximize performance under restrictive thermal budgets. However, current software fails to fully leverage the heterogeneous capabilities of the modern processor, lagging behind hardware advancements. We propose a “hybrid++” BFS algorithm for an accelerated processing unit (APU), a heterogeneous processor which fuses the CPU and GPU cores on a single die. Hybrid++ leverages the strength of CPUs and GPUs for serial and data-parallel execution, respectively, to carefully partition BFS by selecting the appropriate execution-core and graph-traversal direction for every search iteration. Our results illustrate that on a variety of graphs ranging from social- to road-networks, hybrid++ yields a speedup of up to 2× compared to the multithreaded hybrid algorithm. Execution of hybrid++ on the APU is also 2.3× more energy efficient than that on a discrete GPU.