This chapter studies the problem of traversing large graphs using the breadth-first search order on distributed-memory supercomputers. We consider both the traditional level-synchronous top-down algorithm as well as the recently discovered direction optimizing algorithm. We analyze the performance and scalability trade-offs in using different local data structures such as CSR and DCSC, enabling in-node multithreading, and graph decompositions such as 1D and 2D decomposition.
@article{arxiv.1705.04590,
title = {Distributed-Memory Breadth-First Search on Massive Graphs},
author = {Aydin Buluc and Scott Beamer and Kamesh Madduri and Krste Asanovic and David Patterson},
journal= {arXiv preprint arXiv:1705.04590},
year = {2017}
}
Comments
arXiv admin note: text overlap with arXiv:1104.4518