Fast Approximate Distance Queries in Unweighted Graphs Using Bounded Asynchrony

We introduce a new parallel algorithm for approximate breadthfirst ordering of an unweighted graph by using bounded asynchrony to parametrically control both the performance and error of the algorithm. This work is based on the k-level asynchronous (KLA) paradigm that trades expensive global synchronizations in the level-synchronous model for local synchronizations in the asynchronous model, which may result in redundant work. Instead of correcting errors introduced by asynchrony and redoing work as in KLA, in this work we control the amount of work that is redone and thus the amount of error allowed, leading to higher performance at the expense of a loss of precision. Results of an implementation of this algorithm are presented on up to 32,768 cores, showing 2.27x improvement over the exact KLA algorithm and 3.8x improvement over the level-synchronous version with minimal error on several graph inputs.