Abstract
We present and evaluate DMC, a distributed model counter for propositional CNF formulae based on the state-of-the-art sequential model counter D4. DMC can take advantage of a (possibly large) number of sequential model counters running on (possibly heterogeneous) computing units spread over a network of computers. For ensuring an efficient workload distribution, the model counting task is shared between the model counters following a policy close to work stealing. The number and the sizes of the messages which are exchanged by the jobs are kept small. The results obtained show DMC as a much more efficient counter than D4, the distribution of the computation yielding large improvements for some benchmarks. DMC appears also as a serious challenger to the parallel model counter CountAntom and to the distributed model counter dCountAntom.
Cite
CITATION STYLE
Lagniez, J. M., Marquis, P., & Szczepanski, N. (2018). DMC: A distributed model counter. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2018-July, pp. 1331–1338). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/185
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