Accelerating parallel graph computing with speculation

Abstract

Nowadays distributed graph computing is widely used to process large amount of data on the internet. Communication overhead is a critical factor in determining the overall efficiency of graph algorithms. Through speculative prediction of the content of communications, we develop an optimization technique to significantly reduce the amount of communications needed for a class of graph algorithms. We have evaluated our optimization technique using five graph algorithms, Single-source shortest path, Connected Components, PageRank, Diameter, and Random Walk, on the Amazon EC2 clusters using different graph datasets. Our optimized implementations have reduced communication overhead by 21--93% for these algorithms, while keeping the error rates under 5%.