An adaptive cross-architecture combination method for graph traversal

Abstract

Breadth-First Search (BFS) is widely used in many real world applications including computational biology, social networks, and electronic design automation. The combination method, using both top-down and bottom-up techniques, is the most effective BFS approach. However, current combination methods rely on trial-and-error and exhaustive search to locate the optimal switching point, which may cause significant runtime overhead. To solve this problem, we design an adaptive method based on regression analysis to predict an optimal switching point for the combination method at runtime within less than 0.1% of the BFS execution time. Additionally, in order to fully utilize the heterogeneous resources offered by current HPC systems and further improve the performance of the combination method, we propose methodologies to allocate the most suitable computation phases of BFS to the corresponding processing components (i.e. CPUs and accelerators) in the system based on graph information and architecture details. Our adaptive method can predict the switching point with high accuracy (compared with exhaustive search) and achieve up to 695X and 8X speedup over the worst and average case. Our cross-architecture adaptive combination method also improves performance dramatically over the cases conducted on a single architecture.