Embarrassingly Parallel Search

Régin, Jean‐Charles; Rezgui, Mohamed; Malapert, Arnaud

doi:10.1007/978-3-642-40627-0_45

Cited by 57 publications

(59 citation statements)

References 12 publications

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“…problem up into many more pieces than we have cores, so that an even balance is obtained automatically. A similar approach in a constraint programming setting has been used by Régin et al (2013), with very favourable results on a range of standard constraint programming problems (but maximum clique was not considered).…”

Section: The Importance Of Good Work Splittingmentioning

confidence: 99%

The Shape of the Search Tree for the Maximum Clique Problem and the Implications for Parallel Branch and Bound

McCreesh

Prosser

2015

ACM Trans. Parallel Comput.

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Finding a maximum clique in a given graph is one of the fundamental NP-hard problems. We compare two multi-core thread-parallel adaptations of a state-of-the-art branch and bound algorithm for the maximum clique problem, and provide a novel explanation as to why they are successful. We show that load balance is sometimes a problem, but that the interaction of parallel search order and the most likely location of solutions within the search space is often the dominating consideration. We use this explanation to propose a new low-overhead, scalable work splitting mechanism. Our approach uses explicit early diversity to avoid strong commitment to the weakest heuristic advice, and late resplitting for balance. More generally, we argue that for branch and bound, parallel algorithm design should not be performed independently of the underlying sequential algorithm.

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Section: The Importance Of Good Work Splittingmentioning

confidence: 99%

The Shape of the Search Tree for the Maximum Clique Problem and the Implications for Parallel Branch and Bound

McCreesh

Prosser

2015

ACM Trans. Parallel Comput.

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“…In other words, load balancing is automatically obtained in a statistical sense. Interestingly, experiments of [21] have shown that the number of subproblems does not depend on the initial problem but rather on the number of workers. Moreover, they have shown that a good decomposition has to generate more than 30 subproblems per worker.…”

Section: Embarrassingly Parallel Searchmentioning

confidence: 99%

“…Our approach does not require to deal with a set of instances and use some sampling technique that are usually more accurate. It exploits the decomposition proposed by the embarrassingly parallel search (EPS) method recently developed [21,22]. EPS proposes to solve a problem by decomposing it into a large number of subproblems consistent with the propagation (i.e., there is no immediate failure triggered by the initial propagation of a subproblem).…”

Section: Introductionmentioning

confidence: 99%

Parallel Strategies Selection

Palmieri

Régin

Schaus³

2016

Lecture Notes in Computer Science

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We consider the problem of selecting the best variable-value strategy for solving a given problem in constraint programming. We show that the recent Embarrassingly Parallel Search method (EPS) can be used for this purpose. EPS proposes to solve a problem by decomposing it in a lot of subproblems and to give them on-demand to workers which run in parallel. Our method uses a part of these subproblems as a simple sample as defined in statistics for comparing some strategies in order to select the most promising one that will be used for solving the remaining subproblems. For each subproblem of the sample, the parallelism helps us to control the running time of the strategies because it gives us the possibility to introduce timeouts by stopping a strategy when it requires more than twice the time of the best one. Thus, we can deal with the great disparity in solving times for the strategies. The selections we made are based on the Wilcoxon signed rank tests because no assumption has to be made on the distribution of the solving times and because these tests can deal with the censored data that we obtain after introducing timeouts. The experiments we performed on a set of classical benchmarks for satisfaction and optimization problems show that our method obtain good performance by selecting almost all the time the best variable-value strategy and by almost never choosing a variable-value strategy which is dramatically slower than the best one. Our method also outperforms the portfolio approach consisting in running some strategies in parallel and is competitive with the multi armed bandit framework.

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“…Search-space splitting techniques such as domain decomposition could be interesting, but initial experiments [18] show that the speedup tends to flatten after a few tens of cores (e.g., speedup of 28 with 32 cores and 29 with 64 cores, for an all-solution search of the 17-queens problem). A recent approach based on a smaller granularity domain decomposition [63] shows better performance. The results for all-solution search on classical CSPLib benchmarks are quite encouraging and show an average speedup with 40 cores of 14 (Resp.…”

Section: Introductionmentioning

confidence: 99%

Large-scale parallelism for constraint-based local search: the costas array case study

et al. 2014

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We present the parallel implementation of a constraint-based Local Search algorithm and investigate its performance on several hardware platforms with several hundreds or thousands of cores. We chose as the basis for these experiments the Adaptive Search method, an efficient sequential Local Search method for Constraint Satisfaction Problems (CSP). After preliminary experiments on some CSPLib benchmarks, we detail the modeling and solving of a hard combinatorial problem related to radar and sonar applications: the Costas Array Problem. Performance evaluation on some classical CSP benchmarks shows that speedups are very good for a few tens of cores, and good up to a few hundreds of cores. However for a hard combinatorial search problem such as the Costas Array Problem, performance evaluation of the sequential version shows results outperforming previous Local Search implementations, while the parallel version shows nearly linear speedups up to 8,192 cores. The proposed parallel scheme is simple and based on independent multi-walks with no communication between processes during search. We also investigated a cooperative multi-walk scheme where processes share simple information, but this scheme does not seem to improve performance.

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Embarrassingly Parallel Search

Cited by 57 publications

References 12 publications

The Shape of the Search Tree for the Maximum Clique Problem and the Implications for Parallel Branch and Bound

The Shape of the Search Tree for the Maximum Clique Problem and the Implications for Parallel Branch and Bound

Parallel Strategies Selection

Large-scale parallelism for constraint-based local search: the costas array case study

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