A Parallel Exact Solver for the Three-Index Quadratic Assignment Problem

Galea, François; Cun, Bertrand Le

doi:10.1109/ipdps.2011.354

Cited by 4 publications

(6 citation statements)

References 27 publications

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“…In contrast, the landscape of applied parallel programming models is more diverse and includes approaches based on threads, message passing, shared memory and GPGPUs. With regard to the former three models, mostly sublinear or linear speedup has been reported but there are also a few studies [Ponz-Tienda et al, 2017, Borisenko et al, 2011, Galea and Le Cun, 2011] that report superlinear speedup. This speedup can be achieved, for example, when a parallel executed algorithm provides "good" bounds that allow pruning large parts of the search tree at early stages.…”

Section: Exact Algorithmsmentioning

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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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: Exact Algorithmsmentioning

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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“…There exists a variety of papers in the literature that proposes parallel branch-and-bound algorithms or frameworks to ease its development for distributed [43][44][45][46] and shared memory [6,44,[47][48][49][50][51] architectures. However, to the best of our knowledge, few of them explores both shared and distributed memory.…”

Section: Load Balance Of a Parallel Branch-and-bound Based On Multicomentioning

confidence: 99%

Memory aware load balance strategy on a parallel branch‐and‐bound application

Silva

Boeres

Drummond

et al. 2014

Concurrency and Computation

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SUMMARYThe latest trends in high-performance computing systems show an increasing demand on the use of a large scale multicore systems in a efficient way, so that high compute-intensive applications can be executed reasonably well. However, the exploitation of the degree of parallelism available at each multicore component can be limited by the poor utilization of the memory hierarchy available. Actually, the multicore architecture introduces some distinct features that are already observed in shared memory and distributed environments. One example is that subsets of cores can share different subsets of memory. In order to achieve high performance it is imperative that a careful allocation scheme of an application is carried out on the available cores, based on a scheduling model that considers the main performance bottlenecks, as for example, memory contention. In this paper, the Multicore Cluster Model (MCM) is proposed, which captures the most relevant performance characteristics in multicores systems such as the influence of memory hierarchy and contention. Better performance was achieved when a load balance strategy for a Branch-and-Bound application applied to the Partitioning Sets Problem is based on MCM, showing its efficiency and applicability to modern systems.

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“…Although the computational results are encouraging, they also illustrate the level of difficulty associated with the Q3AP. Recently, Galea et al (2008) developed a parallel version of the exact solution algorithm of Hahn et al (2008b). This parallel code is not only an instrument for solving exactly large instances, but will also enable experimentation for improving the runtime of Q3AP exact solution algorithms.…”

Section: Three‐dimensional Assignment Problemsmentioning

confidence: 99%

“…Appearing in the literature more recently are the RLT‐1 QAP exact algorithm by Hahn and Grant (1998), the RLT‐2 QAP exact algorithm by Adams et al (2007), the level‐3 RLT (RLT‐3) QAP lower bound calculations by Hahn et al (2009b), the RLT‐1 exact algorithm for the Q3AP by Hahn et al (2008b), the RLT‐1 parallel exact Q3AP solver by Galea et al (2008) and the RLT‐1 exact algorithms for the GQAP by Hahn et al (2008a) and Pessoa et al (2010).…”

Section: Descriptions Of Apsmentioning

confidence: 99%

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Exact solution of emerging quadratic assignment problems

Hahn

Zhu²,

Guignard

et al. 2010

Int Trans Operational Res

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We report on a growing class of assignment problems that are increasingly of interest and very challenging in terms of the difficulty they pose to attempts at exact solution. These problems address economic issues in the location and design of factories, hospitals, depots, transportation hubs and military bases. Others involve improvements in communication network design. In this article we survey the latest and best methods available for solving exactly these difficult problems and suggest a taxonomy that provides a framework for combining existing solution methods and sets of computer tools that can be modified and extended to make inroads in solving this growing class of optimization problems.

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A Parallel Exact Solver for the Three-Index Quadratic Assignment Problem

Cited by 4 publications

References 27 publications

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

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

Memory aware load balance strategy on a parallel branch‐and‐bound application

Exact solution of emerging quadratic assignment problems

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