Analysis of Subgraph-Centric Distributed Shortest Path Algorithm

Dindokar, Ravikant; Choudhury, Neel; Simmhan, Yogesh

doi:10.1109/ipdpsw.2015.87

Cited by 2 publications

(2 citation statements)

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“…This work extends our previous work [8] that was limited to examining the BFS algorithm for three smaller graphs using a single partitioning strategy. Here, we generalize that approach by formally introducing the meta-graph sketch, exploring three different partitioning strategies, include PageRank in the algorithmic analysis, and validate these results for 5 large graphs with spatial and powerlaw topologies.…”

Section: Introductionsupporting

confidence: 61%

“…This is apparent, since visiting one vertex in each meta-vertex will require a traversal on a meta-edge, and the fewest possible such meta-edges is given by the radius, while the upper bound on the radius is the diameter. However, BFS using the subgraph centric model can have revisits [8], where the shortest path between two vertices in the same subgraph is through a different subgraph. As a result, a loose upper bound for the number of supersteps is the diameter of the whole graph.…”

Section: ) Number Of Superstepsmentioning

confidence: 99%

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A meta-graph approach to analyze subgraph-centric distributed programming models

Dindokar

Choudhury

Simmhan

2016

2016 IEEE International Conference on Big Data (Big Data)

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Component-centric distributed graph processing platforms that use a bulk synchronous parallel (BSP) programming model have gained traction. These address the short-comings of Big Data abstractions/platforms like MapReduce/Hadoop for large-scale graph processing. However, there is limited literature on foundational aspects of the behavior of these component-centric abstractions for different graphs, graph partitioning, and graph algorithms. Here, we propose a analytical approach based on a meta-graph sketch to examine the characteristics of component-centric graph programming models at a coarse granularity. In particular, we apply this sketch to subgraph-and block-centric abstractions, and draw a comparison with vertex-centric models like Google's Pregel. First, we explore the impact of various graph partitioning techniques on the meta-graph, and next consider the impact of the meta-graph on graph algorithms. This decouples the unwieldy large graph and their partitioning specific artifacts from their algorithmic analysis. We use 5 spatial and powerlaw graphs as exemplars, four different partitioning strategies, and PageRank and Breadth First Search as canonical algorithms. These analysis over the meta-graphs provide a reliable measure of the expected number of supersteps, and the communication and computational complexity of the algorithms for various graphs, and the relative merits of subgraph-centric models over vertex-centric ones.

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Section: Introductionsupporting

confidence: 61%

Section: ) Number Of Superstepsmentioning

confidence: 99%