SIMD tabu search for the quadratic assignment problem with graphics hardware acceleration

Zhu, Weihang; Curry, James; Márquez, Alberto

doi:10.1080/00207540802555744

Cited by 36 publications

(30 citation statements)

References 19 publications

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“…In [20], a single instruction multiple data tabu search (SIMD-TS) algorithm is proposed. In [21], a multi-start model for local search algorithms on GPU is discussed.…”

Section: B Related Workmentioning

confidence: 99%

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Implementation of histogram based sampling algorithm within an EDA scheme with CUDA

Tsutsui

Fujimoto

2012

2012 IEEE Congress on Evolutionary Computation

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In this paper, we describe an implementation of Node Histogram Sampling Algorithm (NHBSA) on GPUs with CUDA and apply the algorithm to solve large scale QAP instances. To solve large scale QAP instances, we combined the taboo search with NHBSA. In this implementation, we used an efficient thread assignment method, Move-Cost Adjusted Thread Assignment (MATA), which is proposed in a previous study. Through these experiments, we show that MATA plays an important role for efficient parallel computation in NHBSA. We also show the effectiveness of running NHBSA on multiple GPUs using the island model in independent run mode.

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“…In [20], a single instruction multiple data tabu search (SIMD-TS) algorithm is proposed. In [21], a multi-start model for local search algorithms on GPU is discussed.…”

Section: B Related Workmentioning

confidence: 99%

“…Solving QAP with iterated local search on GPUs can be found in [20], [21], [22]. In [20], a single instruction multiple data tabu search (SIMD-TS) algorithm is proposed.…”

Section: B Related Workmentioning

confidence: 99%

Implementation of histogram based sampling algorithm within an EDA scheme with CUDA

Tsutsui

Fujimoto

2012

2012 IEEE Congress on Evolutionary Computation

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“…In the GPU literature we have found two main approaches. Either, a GPU based parallel neighborhood evaluation of the different local searches is performed sequentially (Luong et al [61]), or, the local searches run in parallel on the GPU (O'Neil et al [70,99], Luong et al [61]). …”

Section: Multi-start Local Searchmentioning

confidence: 99%

“…Another idea, used e.g. in (Zhu et al [99], Luong et al [61]), is to let the LS in each thread run only for a given number of iterations and then perform restart or load balancing before continuing. Due to the many variables involved, it is impossible to state generally that the sequential parallel neighborhood evaluation is better or worse than the one thread per local search approach.…”

Section: Multi-start Local Searchmentioning

confidence: 99%

GPU computing in discrete optimization. Part II: Survey focused on routing problems

Schulz

Hasle

Brodtkorb

et al. 2013

EURO Journal on Transportation and Logistics

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In many cases there is still a large gap between the performance of current optimization technology and the requirements of real world applications. As in the past, performance will improve through a combination of more powerful solution methods and a general performance increase of computers. These factors are not independent. Due to physical limits, hardware development no longer results in higher speed for sequential algorithms, but rather in increased parallelism. Modern commodity PCs include a multi-core CPU and at least one GPU, providing a low cost, easily accessible heterogeneous environment for high performance computing. New solution methods that combine task parallelization and stream processing are needed to fully exploit modern computer architectures and profit from future hardware developments. This paper is the second in a series of two. Part I gives a tutorial style introduction to modern PC architectures and GPU programming. Part II gives a broad survey of the literature on parallel computing in discrete optimization targeted at modern PCs, with special focus on routing problems. We assume that the reader is familiar with GPU programming, and refer the interested reader to Part I. We conclude with lessons learnt, directions for future research, and prospects.

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“…Indeed, there only exists few research works related to evolutionary algorithms on GPU: genetic algorithm [12], [13], genetic programming [14], [15] and evolutionary programming [16], [17]. To the best of our knowledge GPU computing has never deeply investigated for LS algorithms [18], [19]. With the arrival of OpenCL as the open standard programming language on GPU and the arrival of future compilers for this language, like other application areas, combinatorial optimization on GPU will generate a growing interest.…”

Section: Gpu Computing For Metaheuristicsmentioning

confidence: 99%

A GPU-based iterated tabu search for solving the quadratic 3-dimensional assignment problem

Loukil

Melab

Talbi

2010

ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010

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Abstract-The quadratic 3-dimensional assignment problem (Q3AP) is an extension of the well-known NP-hard quadratic assignment problem. It has been proved to be one of the most difficult combinatorial optimization problems. Local search (LS) algorithms are a class of heuristics which have been successfully applied to solve such hard optimization problem. These methods handle with a single solution iteratively improved by exploring its neighborhood in the solution space. In this paper, we propose an iterated tabu search for solving the Q3AP. The design of this algorithm is essentially based on a new large neighborhood structure. Indeed, in LS heuristics, designing operators to explore large promising regions of the search space may improve the quality of the obtained solutions. However, designing such neighborhood is at the expense of a highly computationally process. Therefore, the use of graphics processing units (GPUs) provides an efficient complementary way to speed up the search. The proposed GPU-based iterated tabu search has been experimented on 5 different Q3AP instances. The obtained results are convincing both in terms of efficiency, quality and robustness of the provided solutions at run time.Index Terms-Quadratic 3-dimensional assignment problem (Q3AP), GPU-based local search, metaheuristics on GPU, iterated tabu search on graphics processing units.

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SIMD tabu search for the quadratic assignment problem with graphics hardware acceleration

Cited by 36 publications

References 19 publications

Implementation of histogram based sampling algorithm within an EDA scheme with CUDA

Implementation of histogram based sampling algorithm within an EDA scheme with CUDA

GPU computing in discrete optimization. Part II: Survey focused on routing problems

A GPU-based iterated tabu search for solving the quadratic 3-dimensional assignment problem

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