On Identifying Strongly Connected Components in Parallel

A Divide-and-Conquer Algorithm for Identifying Strongly Connected Components

The strongly connected components of a directed graph can be found in an optimal linear time, by algorithms based on depth first search. Unfortunately, depth first search is difficult to parallelize. We describe two divide-and-conquer algorithms for this problem that have significantly greater potential for parallelization. We show the expected serial runtime of our simpler algorithm to be O(m ...

Finding strongly connected components in distributed graphs

The traditional, serial, algorithm for finding the strongly connected components in a graph is based on depth first search and has complexity which is linear in the size of the graph. Depth first search is difficult to parallelize, which creates a need for a different parallel algorithm for this problem. We describe the implementation of a recently proposed parallel algorithm that finds strongl...

Distributed Calculation of PageRank Using Strongly Connected Components

We provide an approach to distribute the calculation of PageRank, by splitting the graph into its strongly connected components. As we prove, the global ranking may be calculated componentwise, as long as the rankings of pages directly linking to the current component are already known. Depending on the structure of the WWW, this approach approach may be used to calculate the ranking on several...

Parallel Algorithms for Detecting Strongly Connected Components

Efficient GPU algorithms for parallel decomposition of graphs into strongly connected and maximal end components

This article presents parallel algorithms for component decomposition of graph structures on general purpose graphics processing units (GPUs). In particular, we consider the problem of decomposing sparse graphs into strongly connected components, and decomposing graphs induced by stochastic games (such as Markov decision processes) into maximal end components. These problems are key ingredients...