2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (2018)
Vancouver, British Columbia, Canada
May 21, 2018 to May 25, 2018
The future of main memory appears to lie in the direction of new technologies that provide strong capacity-to-performance ratios, but have write operations that are much more expensive than reads in terms of latency, bandwidth, and energy. Motivated by this trend, we propose sequential and parallel algorithms to solve graph connectivity problems using significantly fewer writes than conventional algorithms. Our primary algorithmic tool is the construction of an o(n)-sized implicit decomposition of a bounded-degree graph G on n nodes, which combined with read-only access to G enables fast answers to connectivity and biconnectivity queries on G. The construction breaks the linear-write "barrier", resulting in costs that are asymptotically lower than conventional algorithms while adding only a modest cost to querying time. For general non-sparse graphs on m edges, we also provide the first o(m) writes and O(m) operations parallel algorithms for connectivity and biconnectivity. These algorithms provide insight into how applications can efficiently process computations on large graphs in systems with read-write asymmetry.
computational complexity, graph theory, parallel algorithms, query processing
N. Ben-David et al., "Implicit Decomposition for Write-Efficient Connectivity Algorithms," 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Vancouver, British Columbia, Canada, 2018, pp. 711-722.