This work bridges the gap between distributed and centralised models of computing in the context of sublinear-time graph algorithms. A priori, typical centralised models of computing (e.g., parallel decision trees or centralised local algorithms) seem to be much more powerful than distributed message-passing algorithms: centralised algorithms can directly probe any part of the input, while in distributed algorithms nodes can only communicate with their immediate neighbours. We show that for a large class of graph problems, this extra freedom does not help centralised algorithms at all: efficient stateless deterministic centralised local algorithms can be simulated with efficient distributed message-passing algorithms. In particular, this enables us to transfer existing lower bound results from distributed algorithms to centralised local algorithms.
|Title of host publication||Distributed Computing - 30th International Symposium, DISC 2016, Proceedings|
|Editors||Cyril Gavoille, David Ilcinkas|
|Number of pages||14|
|State||Published - 2016|
|Event||30th International Symposium on Distributed Computing, DISC 2016 - Paris, France|
Duration: 27 Sep 2016 → 29 Sep 2016
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||30th International Symposium on Distributed Computing, DISC 2016|
|Period||27/09/16 → 29/09/16|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2016.