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Algorithms for computing All-Pairs Shortest-Paths (APSP) are criticalbuilding blocks underlying many practical applications. The standard sequentialalgorithms, such as Floyd-Warshall and Johnson, quickly become infeasible forlarge input graphs, necessitating parallel approaches. In this work, we providedetailed analysis of parallel APSP performance on distributed memory clusterswith Apache Spark. The Spark model allows for a portable and easy to deploydistributed implementation, and hence is attractive from the end-user point ofview. We propose four different APSP implementations for large undirectedweighted graphs, which differ in complexity and degree of reliance ontechniques outside of pure Spark API. We demonstrate that Spark is able tohandle APSP problems with over 200,000 vertices on a 1024-core cluster, and cancompete with a naive MPI-based solution. However, our best performing solverrequires auxiliary shared persistent storage, and is over two times slower thanoptimized MPI-based solver.

Solving All-Pairs Shortest-Paths Problem in Large Graphs Using Apache Spark