Optimal speedup of Las Vegas algorithms
Information Processing Letters
Parallel programming in OpenMP
Parallel programming in OpenMP
A constraint-based architecture for local search
OOPSLA '02 Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
OpenMP: An Industry-Standard API for Shared-Memory Programming
IEEE Computational Science & Engineering
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Search Procedures and Parallelism in Constraint Programming
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Constraint-Based Local Search
Transparent Parallelization of Constraint Programming
INFORMS Journal on Computing
Parallel constraint-based local search on the HA8000 supercomputer (abstract)
Proceedings of the 2011 ACM Symposium on Applied Computing
Experiments in parallel constraint-based local search
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Targeting the Cell Broadband Engine for constraint-based local search
Concurrency and Computation: Practice & Experience
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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 paper presents 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 of machines. Of particular interest is the observation that generic abstractions of CBLS and CP, such as models and solutions, and advanced control structures such as events and closures, play a fundamental role to keep the distance between sequential and distributed CBLS programs small. As a result, the abstractions directly apply to CP programs using multistarts or restarts procedures.