Distributed Constraint-Based Local Search

Distributed computing is increasingly important at a time when the doubling of the number of transistors on a processor every 18 months no longer translates in a doubling of speed but instead a doubling of the number of cores. Unfortunately, it also places significant conceptual and implementation burden on programmers. This paper aims at addressing this challenge for constraint-based local search (CBLS), whose search procedures typically exhibit inherent parallelism stemming from multistart, restart, or population-based techniques whose benefits have been demonstrated both experimentally and theoretically. The paents abstractions that allows distributed CBLS programs to be close to their sequential and parallel counterparts, keeping the conceptual and implementation overhead of distributed computing minimal. A preliminary implementation in Comet exhibits significant speed-ups in constraint satisfaction and optimization applications. The implementation also scales well with the number ...

• Type: Conference
• Year: 2006
• Where: CP
• Authors: Laurent Michel, Andrew See, Pascal Van Hentenryck
• Keywords: Artificial Intelligence, CP 2006, Distributed Computing, Implementation, Implementation Burden