Solving quadratic assignment problems by genetic algorithms with GPU computation

Tsutsui, Shigeyoshi; Fujimoto, Noriyuki

doi:10.1145/1570256.1570355

Cited by 75 publications

(31 citation statements)

References 28 publications

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“…Real life like instances and randomly generated instances of the QAPLIB library whose size ranges from 50 to 150 have been considered. Speedups of 24.6 times have been observed as compared with a sequential version of the method implemented on the CPU (reference is also made to [41]). …”

Section: B Scheduling Problems 1) Branch and Boundmentioning

confidence: 99%

Recent Advances on GPU Computing in Operations Research

Boyer

Baz

2013

2013 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum

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Abstract-In the last decade, Graphics Processing Units (GPUs) have gained an increasing popularity as accelerators for High Performance Computing (HPC) applications. Recent GPUs are not only powerful graphics engines but also highly threaded parallel computing processors that can achieve sustainable speedup as compared with CPUs. In this context, researchers try to exploit the capability of this architecture to solve difficult problems in many domains in science and engineering. In this article, we present recent advances on GPU Computing in Operations Research. We focus in particular on Integer Programming and Linear Programming.

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Section: B Scheduling Problems 1) Branch and Boundmentioning

confidence: 99%

Recent Advances on GPU Computing in Operations Research

Boyer

Baz

2013

2013 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum

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“…Tsutsui et al [41] propose run a coarse-grained GA on GPU to solve the quadratic assignment problem (QAP) using CUDA. This is one of the hardest optimization problems in permutation domains.…”

Section: Coarse-grained Approaches (Island Model)mentioning

confidence: 99%

GPU Parallel Computation in Bioinspired Algorithms: A Review

Arenas

Romero

Mora

et al. 2012

Advances in Intelligent Modelling and Simulation

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Abstract. As bioinspired methods usually need a high amount of computational resources, parallelization is an interesting alternative in order to decrease the execution time and to provide accurate results. In this sense, recently there has been a growing interest in developing parallel algorithms using graphic processing units (GPU) also referred as GPU computation. Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units that possess more memory bandwidth and computational capability than central processing units (CPUs). As GPUs are available in personal computers, and they are easy to use and manage through several GPU programming languages, graphics engines are being adopted widely in scientific computing applications, particularly in the fields of computational biology and bioinformatics. This chapter reviews the use of GPUs to solve scientific problems, giving an overview of current software systems.

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“…Tsutsui et al [22] propose run a coarse-grained GA on GPU to solve the quadratic assignment problem (QAP) using CUDA. This is one of the hardest optimization problems in permutation domains.…”

Section: Coarse-grained Approaches (Island Model)mentioning

confidence: 99%

GPU Computation in Bioinspired Algorithms: A Review

Arenas

Mora

Romero

et al. 2011

Advances in Computational Intelligence

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Abstract. Bioinspired methods usually need a high amount of computational resources. For this reason, parallelization is an interesting alternative in order to decrease the execution time and to provide accurate results. In this sense, recently there has been a growing interest in developing parallel algorithms using graphic processing units (GPU) also refered as GPU computation. Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units (GPUs) that possess more memory bandwidth and computational capability than central processing units (CPUs). As GPUs are available in personal computers, and they are easy to use and manage through several GPU programming languages (CUDA, OpenCL, etc.), graphics engines are being adopted widely in scientific computing applications, particularly in the fields of computational biology and bioinformatics. This paper reviews the use of GPUs to solve scientific problems, giving an overview of current software systems.

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Solving quadratic assignment problems by genetic algorithms with GPU computation

Cited by 75 publications

References 28 publications

Recent Advances on GPU Computing in Operations Research

Recent Advances on GPU Computing in Operations Research

GPU Parallel Computation in Bioinspired Algorithms: A Review

GPU Computation in Bioinspired Algorithms: A Review

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