DMC: A Distributed Model Counter

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

Text

Lagniez et al. "DMC: A Distributed Model Counter." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/185

Markdown

[Lagniez et al. "DMC: A Distributed Model Counter." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/lagniez2018ijcai-dmc/) doi:10.24963/IJCAI.2018/185

BibTeX

@inproceedings{lagniez2018ijcai-dmc,
  title     = {{DMC: A Distributed Model Counter}},
  author    = {Lagniez, Jean-Marie and Marquis, Pierre and Szczepanski, Nicolas},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {1331-1338},
  doi       = {10.24963/IJCAI.2018/185},
  url       = {https://mlanthology.org/ijcai/2018/lagniez2018ijcai-dmc/}
}