A Case Study in Complexity Estimation: Towards Parallel Branch-and-Bound over Graphical Models
Abstract
We study the problem of complexity estimation in the context of parallelizing an advanced Branch and Bound-type algorithm over graphical models. The algorithm's pruning power makes load balancing, one crucial element of every distributed system, very challenging. We propose using a statistical regression model to identify and tackle disproportionally complex parallel subproblems, the cause of load imbalance, ahead of time. The proposed model is evaluated and analyzed on various levels and shown to yield robust predictions. We then demonstrate its effectiveness for load balancing in practice.
Cite
Text
Otten and Dechter. "A Case Study in Complexity Estimation: Towards Parallel Branch-and-Bound over Graphical Models." Conference on Uncertainty in Artificial Intelligence, 2012.Markdown
[Otten and Dechter. "A Case Study in Complexity Estimation: Towards Parallel Branch-and-Bound over Graphical Models." Conference on Uncertainty in Artificial Intelligence, 2012.](https://mlanthology.org/uai/2012/otten2012uai-case/)BibTeX
@inproceedings{otten2012uai-case,
title = {{A Case Study in Complexity Estimation: Towards Parallel Branch-and-Bound over Graphical Models}},
author = {Otten, Lars and Dechter, Rina},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2012},
pages = {665-674},
url = {https://mlanthology.org/uai/2012/otten2012uai-case/}
}