A SIMD Solution for the Quadratic Assignment Problem with GPU Acceleration

Chaparala, Abhilash; Novoa, Clara; Qasem, Apan

doi:10.1145/2616498.2616521

Cited by 6 publications

(6 citation statements)

References 26 publications

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“…We conclude that GPU and dynamic parallelism are attractive tools to use in heuristic searches. We solved successfully the accuracy issue we had when running the Taixxa instances with our accelerated 2-Opt algorithm described in [9]. We used an elementary tabu search method that do not use intensification of further diversification strategies besides the one implemented in the variable size tabu list.…”

Section: Discussionmentioning

confidence: 99%

“…The solution finds the values of all variables x ki that minimize the sum of the products of flows and distances (Eq. 2), assign each unit k to a single location i, and leave each location i with only one unit k assigned (See assignment constraints in [16,9]). …”

Section: Quadratic 0-1 Formulationmentioning

confidence: 99%

“…We recall that our work in [9] compared the GPU accelerated 2-opt algorithm to an OpenMP based CPU implementation. The speedup factor achieved was at least 16.…”

Section: Performance and Accuracymentioning

confidence: 99%

“…In our computational study, we investigate the performance and accuracy of the proposed tabu search GPU implementation. The performance is assessed by comparing our implementation to a parallel 2-opt algorithm recently implemented by the authors of this paper [9] and to the tabu search GPU implementation described in [26]. The accuracy is studied by comparing the best results from our tabu search implementation vs. the best known solutions posted in QAPLIB.…”

Section: Introductionmentioning

confidence: 99%

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ASIMD tabu searchimplementation for solving the quadratic assignment problem with GPU acceleration

Novoa¹,

Qasem²,

Chaparala³

2015

Proceedings of the 2015 XSEDE Conference on Scientific Advancements Enabled by Enhanced Cyberinfrastructure - XSEDE '15

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In the Quadratic Assignment Problem (QAP), n units (usually departments , machines, or electronic components) must be assigned to n locations given the distance between the locations and the flow between the units. The goal is to find the assignment that minimizes the sum of the products of distance traveled and flow between units. The QAP is a combinatorial problem difficult to solve to optimality even for problems where n is relatively small (e.g., n = 30). In this paper, we develop a parallel tabu search algorithm to solve the QAP and leverage the compute capabilities of current GPUs. The single instruction multiple data (SIMD) algorithm is implemented on the Stampede cluster hosted by the Texas Advanced Computing Center (TACC) at the University of Texas at Austin. We enhance our implementation by exploiting the dynamic parallelism made available in the Nvidia Kepler high performance computing architecture. On a series of experiments on the well-known QAPLIB data sets, our algorithm produces solutions that are as good as the best known ones posted in QAPLIB. The worst case percentage of accuracy we obtained was 0.83%. Given the applicability of QAP, our algorithm has very good potential to accelerate scholarly research in Engineering, in the fields of Operations Research and design of electronic devices. To the best of our knowledge, this work is the first to successfully parallelize the tabu search metaheuristic to solve the QAP with the recency-based feature, implemented serially in [10]. Our work is also the first to exploit GPU dynamic parallelism in a tabu search metaheuristic to solve the QAP.

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Section: Discussionmentioning

confidence: 99%

Section: Quadratic 0-1 Formulationmentioning

confidence: 99%

“…We recall that our work in [9] compared the GPU accelerated 2-opt algorithm to an OpenMP based CPU implementation. The speedup factor achieved was at least 16.…”

Section: Performance and Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ASIMD tabu searchimplementation for solving the quadratic assignment problem with GPU acceleration

Novoa¹,

Qasem²,

Chaparala³

2015

Proceedings of the 2015 XSEDE Conference on Scientific Advancements Enabled by Enhanced Cyberinfrastructure - XSEDE '15

Self Cite

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“…Okano et al (2018) proposed study that investigates the local search techniques on QAP problem and divided 2opt into two strategies which are first improvement and best improvement. In addition, (Chaparala et al, 2014) conducted study that applies 2-opt on QAP to compute capabilities of current GPUs in which, 2-opt demonstrates computed cost is within the 0.1% of best known value and results are gathered in reasonable time even for large datasets. In terms of tabu search applications in QAP, (Zhu et al, 2010) proposes a single instruction multiple data tabu search by utilizing graphic hardware acceleration on QAP and they achieved the reach solution 20-45 time faster than standard CPU code.…”

Section: Introductionmentioning

confidence: 99%

Layout Optimization in a Fitness Industry: An Application of QAP Formulation and Heuristic Approaches

Eligüzel¹,

Özceylan²,

Atasagun³

2021

Journal of Computer Science

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The Quadratic Assignment Problem (QAP) is the problem of determining a one-to-one and onto assignment between two sets, each consisting of n objects (e.g., n facilities and n locations) so as to minimize the sum of the distance  flow associated with pairs of assignments. In this study, QAP formulation is applied to a fitness center to assign the sports equipment to the related locations on the layout. Facilities are considered as sports equipment and locations are considered the required area for equipment on the layout. A case study with 19 sports equipment is considered. The objective of the study is to allocate the equipment to the 19 available locations to minimize the total walking distance while considering the interactions between sports equipment. An Integer Programming (IP) formulation is applied using GUROBI package program. 2-opt and tabu search algorithms are also applied to find the feasible/optimal solutions in a reasonable time. The results show that the heuristic approaches are more effective than IP in terms of computational time. The optimal result which is obtained by IP, 2-opt and tabu search provides a better layout with 55.47% less distance  flow compared to the current layout.

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A cooperative GPU-based Parallel Multistart Simulated Annealing algorithm for Quadratic Assignment Problem

Sonuç

Şen

Bayir

2018

Engineering Science and Technology, an International Journal

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A SIMD Solution for the Quadratic Assignment Problem with GPU Acceleration

Cited by 6 publications

References 26 publications

ASIMD tabu searchimplementation for solving the quadratic assignment problem with GPU acceleration

ASIMD tabu searchimplementation for solving the quadratic assignment problem with GPU acceleration

Layout Optimization in a Fitness Industry: An Application of QAP Formulation and Heuristic Approaches

A cooperative GPU-based Parallel Multistart Simulated Annealing algorithm for Quadratic Assignment Problem

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