Parallel Branch-and-Branch Algorithms: Survey and Synthesis

Gendron, Bernard; Crainic, Teodor Gabriel

doi:10.1287/opre.42.6.1042

Cited by 288 publications

(155 citation statements)

References 90 publications

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“…This model is based on the dynamic generation/destruction of threads with (asynchronous) multiple sets [13]. Each thread handles its own working set.…”

Section: Pthreads Modelmentioning

confidence: 99%

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On parallel Branch and Bound frameworks for Global Optimization

et al. 2017

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Branch and Bound (B&B) algorithms are known to exhibit an irregularity of the search tree. Therefore, developing a parallel approach for this kind of algorithms is a challenge. The efficiency of a B&B algorithm depends on the chosen Branching, Bounding, Selection, Rejection, and Termination rules. The question we investigate is how the chosen platform consisting of programming language, used libraries, or skeletons influences programming effort and algorithm performance. Selection rule and data management structures are usually hidden to programmers for frameworks with a high level of abstraction, as well as the load balancing strategy, when the algorithm is run in parallel. We investigate the question by implementing a multidimensional Global Optimization B&B algorithm with the help of three frameworks with a different level of abstraction (from more to less): Bobpp, Threading Building Blocks (TBB), and a customized Pthread implementation. The following has been found. The Bobpp implementation is easy to code, but exhibits the poorest scalability. On the contrast, the TBB and Pthread implementations scale almost linearly on the used platform. The TBB approach shows a slightly better productivity.

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“…This model is based on the dynamic generation/destruction of threads with (asynchronous) multiple sets [13]. Each thread handles its own working set.…”

Section: Pthreads Modelmentioning

confidence: 99%

“…Thanks to parallel computation, a larger number of this type of problems can be solved in a reasonable computational time [29,39]. Parallel B&B algorithms can be classified based on several characteristics [13]. One of them is the distinction between the use of single and multiple working sets.…”

Section: Introductionmentioning

confidence: 99%

On parallel Branch and Bound frameworks for Global Optimization

et al. 2017

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“…Branch and Bound: Many work reported by the survey in [1] are based on a centralized approach with a single manager, which maintains the whole tree and hands out tasks to workers. This kind of approach clearly does not scale for grid environments.Aida and al.…”

Section: Related Workmentioning

confidence: 99%

“…However, it is relatively easy to provide parallel implementations of B&B. Many previous work deal with parallel B&B as reported in [1].…”

Section: Introductionmentioning

confidence: 99%

Grid’BnB: A Parallel Branch and Bound Framework for Grids

Caromel

Costanzo

Baduel

et al. 2007

High Performance Computing – HiPC 2007

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Abstract. This article presents Grid'BnB, a parallel branch and bound framework for grids. Branch and bound (B&B) algorithms find optimal solutions of search problems and NP-hard optimization problems. Grid'BnB is a Java framework that helps programmers to distribute problems over grids by hiding distribution issues. It is built over a masterworker approach and provides a transparent communication system among tasks. This work also introduces a new mechanism to localize computational nodes on the deployed grid. With this mechanism, we can determine if two nodes are on the same cluster. This mechanism is used in Grid'BnB to reduce inter-cluster communications. We run experiments on a nationwide grid. With this test bed, we analyze the behavior of a communicant application deployed on a large-scale grid that solves the flow-shop problem.

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“…However, the exploration time remains considerable, and using parallel processing is one of the major and popular ways to reduce it. Many parallel B&B approaches have been proposed in the literature [4,2,5]. A taxonomy of associated parallel models is presented in [7].…”

Section: Introductionmentioning

confidence: 99%

A Grid-enabled Branch and Bound Algorithm for Solving Challenging Combinatorial Optimization Problems

Mezmaz

Melab

Talbi

2007

2007 IEEE International Parallel and Distributed Processing Symposium

109

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Solving optimally large instances of combinatorial optimization problems requires a huge amount of computational resources. In this paper, we propose an adaptation of the parallel Branch and Bound algorithm for computational grids. Such gridification is based on new ways to efficiently deal with some crucial issues, mainly dynamic adaptive load balancing, fault tolerance, global information sharing and termination detection of the algorithm. A new efficient coding of the work units (search sub-trees) distributed during the exploration of the search tree is proposed to optimize the involved communications. The algorithm has been implemented following a large scale idle time stealing paradigm (Farmer-Worker). It has been experimented on a Flow-Shop problem instance () that has never been optimally solved. The new algorithm allowed to realize a success story as the optimal solution has been found with proof of optimality, within days using about processors belonging to Nation-wide distinct clusters (administration domains). During the resolution, the worker processors were exploited with an average of while the farmer processor was exploited only of the time. These two rates are good indicators on the efficiency of the proposed approach and its scalability.

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Parallel Branch-and-Branch Algorithms: Survey and Synthesis

Cited by 288 publications

References 90 publications

On parallel Branch and Bound frameworks for Global Optimization

On parallel Branch and Bound frameworks for Global Optimization

Grid’BnB: A Parallel Branch and Bound Framework for Grids

A Grid-enabled Branch and Bound Algorithm for Solving Challenging Combinatorial Optimization Problems

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