Observations from Parallelising Three Maximum Common (Connected) Subgraph Algorithms

Ruth Hoffmann 1 Ciaran Mccreesh 2 Samba Ndojh Ndiaye 3 Patrick Prosser 2 Craig Reilly 2 Christine Solnon 3 James Trimble 2
1 University of St Andrews [Scotland]
2 University of Glasgow
3 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : We discuss our experiences adapting three recent algorithms for maximum common (connected) subgraph problems to exploit multi-core parallelism. These algorithms do not easily lend themselves to parallel search, as the search trees are extremely irregular, making balanced work distribution hard, and runtimes are very sensitive to value-ordering heuristic behaviour. Nonetheless, our results show that each algorithm can be parallelised successfully, with the threaded algorithms we create being clearly better than the sequential ones. We then look in more detail at the results, and discuss how speedups should be measured for this kind of algorithm. Because of the difficulty in quantifying an average speedup when so-called anomalous speedups (superlinear and sublinear) are common, we propose a new measure called aggregate speedup.
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Submitted on : Saturday, March 10, 2018 - 9:00:13 AM
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Ruth Hoffmann, Ciaran Mccreesh, Samba Ndojh Ndiaye, Patrick Prosser, Craig Reilly, et al.. Observations from Parallelising Three Maximum Common (Connected) Subgraph Algorithms. 15th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2018, Delft, Netherlands. pp.298-315. ⟨hal-01728184⟩

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