Dynamic and hierarchical load-balancing techniques applied to parallel branch-and-bound methods

Herrera, Juan F. R.; Casado, Leocadio G.; Hendrix, E.M.T.; Paulavičius, Remigijus; Ilinskas, Julius

doi:10.1109/3pgcic.2013.85

Cited by 4 publications

(3 citation statements)

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“…The appropriateness of a communication topology needs to be linked to the particular algorithm and the applied parallelization strategy so that no general recommendations are appropriate. However, in the sample of computational studies we found only a few studies (e.g., [Mezmaz et al, 2014, Herrera et al, 2013, Rashid et al, 2010, Aydin and Sevkli, 2008) that have implemented more than one topology for one parallelization strategy of a particular algorithm. This low number calls for more studies that investigate multiple topologies for particular combinations of algorithms and parallelization strategies (recommendation 3f).…”

Section: Algorithmic Parallelization and Computational Parallelizationmentioning

confidence: 99%

“…Branch & X [Mezmaz et al, 2014, Chakroun et al, 2013b, Herrera et al, 2017, Taoka et al, 2008, Ponz-Tienda et al, 2017, Ismail et al, 2014, Paulavicius et al, 2011, Christou and Vassilaras, 2013, McCreesh and Prosser, 2015, Eckstein et al, 2015, Carvajal et al, 2014, Borisenko et al, 2017, Gmys et al, 2017, Liu and Kao, 2013, Bak et al, 2011, Gmys et al, 2016, Silva et al, 2015, Barreto and Bauer, 2010, Vu and Derbel, 2016, Chakroun and Melab, 2015, Paulavičius and Žilinskas, 2009, Posypkin and Sigal, 2008, Chakroun et al, 2013a, Aitzai and Boudhar, 2013, Ozden et al, 2017, Cauley et al, 2011, Xu et al, 2009, Aldasoro et al, 2017, Pages-Bernaus et al, 2015, Lubin et al, 2013, Adel et al, 2016, Borisenko et al, 2011, Boukedjar et al, 2012, Carneiro et al, 2011, Galea and Le Cun, 2011, Herrera et al, 2013, Sanjuan-Estrada et al, 2011] Dynamic programming [Dias et al, 2013, Aldasoro et al, 2015…”

Section: Algorithm Typementioning

confidence: 99%

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Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

Schryen

2020

European Journal of Operational Research

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Solving optimization problems with parallel algorithms has a long tradition in OR. Its future relevance for solving hard optimization problems in many fields, including finance, logistics, production and design, is leveraged through the increasing availability of powerful computing capabilities. Acknowledging the existence of several literature reviews on parallel optimization, we did not find reviews that cover the most recent literature on the parallelization of both exact and (meta)heuristic methods. However, in the past decade substantial advancements in parallel computing capabilities have been achieved and used by OR scholars so that an overview of modern parallel optimization in OR that accounts for these advancements is beneficial. Another issue from previous reviews results from their adoption of different foci so that concepts used to describe and structure prior literature differ. This heterogeneity is accompanied by a lack of unifying frameworks for parallel optimization across methodologies, application fields and problems, and it has finally led to an overall fragmented picture of what has been achieved and still needs to be done in parallel optimization in OR. This review addresses the aforementioned issues with three contributions: First, we suggest a new integrative framework of parallel computational optimization across optimization problems, algorithms and application domains. The framework integrates the perspectives of algorithmic design and computational implementation of parallel optimization. Second, we apply the framework to synthesize prior research on parallel optimization in OR, focusing on computational studies published in the period 2008-2017. Finally, we suggest research directions for parallel optimization in OR.Keywords computing science · parallel optimization · computational optimization · literature review * I am grateful for the support provided by Abdullah Burak, Philip Empl, Constanze Hilmer, Gerhard Rauchecker, Richard Schuster, Henning Siemes, and Melih Yilmaz, who supported me substantially in searching and coding research articles.2 Impressive computational results of applying parallelization to the traveling salesman problem (TSP) are reported by Crainic et al. [2006, p.2]. arXiv:1910.03028v1 [cs.DC] 3 Oct 2019Parallel computational optimization in operations research is challenging in general from both the algorithmic and the computational perspective, and ii) a viable alternative to parallelizing algorithms has been the exploitation of ongoing increases of clock speed of single CPUs of modern microprocessors. But this growth process reached a limit already several years ago due to heat dissipation and energy consumption issues . This development makes parallelization efforts (not only in optimization) much more important than it was in earlier times.Fortunately, the need for parallelization has been acknowledged and accompanied by an increased availability of parallel computing resources. This availability is rooted in two phenomena: a) the rapid development of para...

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Section: Algorithmic Parallelization and Computational Parallelizationmentioning

confidence: 99%

Section: Algorithm Typementioning

confidence: 99%

Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

Schryen

2020

European Journal of Operational Research

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“…As selection rule, depth-first search is used in order to reduce memory requirements and facilitate the memory management [17]. The selection rule will be discussed in Sect.…”

Section: Algorithm 1 Simplicial Bandb Algorithmmentioning

confidence: 99%

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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