Work

Parallel Restarted Search

Year: 2014

Type: conference-paper

Abstract: We consider the problem of parallelizing restarted backtrack search. With few notable exceptions, most commercial and academic constraint programming solvers do not learn no-goods during search. Depending on the branching heuristics used, this means that there are little to no side-effects between restarts, making them an excellent target for parallelization. We develop a simple technique for parallelizing restarted search deterministically and demonstrate experimentally that we can achieve near-linear speed-ups in practice.

Source: Proceedings of the AAAI Conference on Artificial Intelligence

DOI: 10.1609/aaai.v28i1.8848

Authors: Andre Cire, Serdar Kadioglu, Meinolf Sellmann

Institutions: Carnegie Mellon University, Oracle (United States), IBM (United States), IBM Research - Thomas J. Watson Research Center

Language: English

Topic: Constraint Satisfaction and Optimization

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Brown University

OpenAlex ID: W80412914

FWCI: 0.968

Cites: 23

Cited by: 17

Related to: 10