Brief Announcement: Massively Parallel Approximate Distance Sketches

Michael Dinitz, Yasamin Nazari, Michael Wagner
2019 International Symposium on Distributed Computing  
Data structures that allow efficient distance estimation have been extensively studied both in centralized models and classical distributed models. We initiate their study in newer (and arguably more realistic) models of distributed computation: the Congested Clique model and the Massively Parallel Computation (MPC) model. In MPC we give two main results: an algorithm that constructs stretch/space optimal distance sketches but takes a (small) polynomial number of rounds, and an algorithm that
more » ... nstructs distance sketches with worse stretch but that only takes polylogarithmic rounds. Along the way, we show that other useful combinatorial structures can also be computed in MPC. In particular, one key component we use is an MPC construction of the hopsets of [2] . This result has additional applications such as the first polylogarithmic time algorithm for constant approximate single-source shortest paths for weighted graphs in the low memory MPC setting.
doi:10.4230/lipics.disc.2019.42 dblp:conf/wdag/DinitzN19 fatcat:3xjwtruhrvfbllwl5lwflt4pcu