Sequential and parallel solution-biased search for subgraph algorithms
| Autor: | James Trimble, Blair Archibald, Ruth Hoffmann, Fraser Dunlop, Ciaran McCreesh, Patrick Prosser |
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| Přispěvatelé: | Rousseau, Louis-Martin, Stergiou, Kostas, EPSRC, University of St Andrews. School of Computer Science, University of St Andrews. St Andrews GAP Centre, University of St Andrews. Pure Mathematics |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Speedup
Degree (graph theory) Heuristic (computer science) Backtracking Computer science Subgraph isomorphism problem Induced subgraph DAS 0102 computer and information sciences 02 engineering and technology 01 natural sciences Theoretical Computer Science 010201 computation theory & mathematics Simple (abstract algebra) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing State (computer science) Algorithm Computer Science(all) |
| Zdroj: | Integration of Constraint Programming, Artificial Intelligence, and Operations Research ISBN: 9783030192112 CPAIOR |
| ISSN: | 0302-9743 |
| Popis: | Funding: This work was supported by the Engineering and Physical Sciences Research Council (grant numbers EP/P026842/1, EP/M508056/1, and EP/N007565). The current state of the art in subgraph isomorphism solving involves using degree as a value-ordering heuristic to direct backtracking search. Such a search makes a heavy commitment to the first branching choice, which is often incorrect. To mitigate this, we introduce and evaluate a new approach, which we call “solution-biased search”. By combining a slightly-random value-ordering heuristic, rapid restarts, and nogood recording, we design an algorithm which instead uses degree to direct the proportion of search effort spent in different subproblems. This increases performance by two orders of magnitude on satisfiable instances, whilst not affecting performance on unsatisfiable instances. This algorithm can also be parallelised in a very simple but effective way: across both satisfiable and unsatisfiable instances, we get a further speedup of over thirty from thirty-six cores, and over one hundred from ten distributed-memory hosts. Finally, we show that solution-biased search is also suitable for optimisation problems, by using it to improve two maximum common induced subgraph algorithms. Postprint |
| Databáze: | OpenAIRE |
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