We propose the Embarrassingly Parallel Search, a simple and efficient method for solving constraint programming problems in parallel. We split the initial problem into a huge number of independent subproblems and solve them with available workers, for instance cores of machines. The decomposition into subproblems is computed by selecting a subset of variables and by enumerating the combinations...

Industrial optimization applications must be robust: they must provide good solutions to problem instances of different size and numerical characteristics, and must continue to work well when side constraints are added. A good testbed for constructing such a robust solver is a set of network design problem instances recently made public by France Telecom. Together, these instances comprise the ...

Backtracking strategies based on the computation of discrepancies have proved themselves successful at solving large problems. They show really good performance when provided with a high-quality domain-specific branching heuristic (variable and value ordering heuristic), which is the case for many industrial problems. We propose a novel approach (PDS) that allows parallelizing a strategy based ...

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 p...

We present Scalable Parallel Depth-First Proof Number Search, a new shared-memory parallel version of depth-first proof number search. Based on the serial DFPN 1+ε method of Pawlewicz and Lew, SPDFPN searches effectively even as the transposition table becomes almost full, and so can solve large problems. To assign jobs to threads, SPDFPN uses proof and disproof numbers and two parameters. SPDF...

SYNCHEM is a large expert system in the domain of organic chemistry. It finds synthetic pathways by chaining backwards from the target molecule to available compounds. SYNCHEM uses heuristic search to explore the solution space efficiently. Depending on the complexity of the target compound, the system currently takes from a few hours to several days.to solve an interesting problem using the pr...

Local search metaheuristics are a recognized means of solving hard combinatorial problems. Over the last couple of decades, significant advances have been made in terms of the formalization, applicability and performance of these methods. Key to the performance aspect is the increased availability of parallel hardware, which turns out to be largely exploitable by this class of procedures. As re...

In general, neural networks are regarded as models for massively parallel computation. But very often, this parallelism is rather limited, especially when considering symmetric networks. For instance, Hoppeld networks do not really compute in parallel as their updating algorithm always requires sequential execution. We describe a recurrent network corresponding to a symmetric network and introd...

There is a big need for the parallelisation of genetic algorithms. In this paper, a heterogeneous framework for the global parallelisation of genetic algorithms is presented. The framework uses a static all-worker parallel programming paradigm based on collective communication. It follows the single program multiple data parallel programming model. It utilises the power of parallel machines by ...

Search in constraint programming is a time consuming task. Search can be speeded up by exploring subtrees of a search tree in parallel. This paper presents distributed search engines that achieve parallelism by distribution across networked computers. The main point of the paper is a simple design of the parallel search engine. Simplicity comes as an immediate consequence of clearly separating ...