Many emerging large-scale data science applications require searching large graphs distributed across multiple memories and processors. This paper presents a distributed breadth...
Andy Yoo, Edmond Chow, Keith W. Henderson, Will Mc...
In this paper we work on the parallelization of the inherently serial Dijkstra's algorithm on modern multicore platforms. Dijkstra's algorithm is a greedy algorithm that ...
Graph algorithms are becoming increasingly important for solving many problems in scientific computing, data mining and other domains. As these problems grow in scale, parallel c...
Andrew Lumsdaine, Douglas Gregor, Bruce Hendrickso...
Abstract. This paper presents new efficient parallel algorithms for finding approximate solutions to graph coloring problems. We consider an existing shared memory parallel graph...
Assefaw Hadish Gebremedhin, Fredrik Manne, Tom Woo...
Distributed processing of real-world graphs is challenging due to their size and the inherent irregular structure of graph computations. We present HIPG, a distributed framework th...
Elzbieta Krepska, Thilo Kielmann, Wan Fokkink, Hen...