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Scalable matrix computations on large scale-free graphs using 2D graph partitioning
2013
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '13
Scalable parallel computing is essential for processing large scale-free (power-law) graphs. The distribution of data across processes becomes important on distributed-memory computers with thousands of cores. It has been shown that twodimensional layouts (edge partitioning) can have significant advantages over traditional one-dimensional layouts. However, simple 2D block distribution does not use the structure of the graph, and more advanced 2D partitioning methods are too expensive for large
doi:10.1145/2503210.2503293
dblp:conf/sc/BomanDR13
fatcat:g5w2n53zzzelfnuourogfum3ii