MAX-MIN Ant System on GPU with CUDA

Bai, Hongliang; Ouyang, Dantong; Li, Ximing; He, Lin; Yu, Hongfeng

doi:10.1109/icicic.2009.255

Cited by 43 publications

(29 citation statements)

References 11 publications

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“…Surprisingly only modest speed up were reported initially [26]. GPU work using artificial ant pheromone trails appears to have concentrated upon NP-hard problems like the travelling salesman problem.…”

Section: Ant Colony Optimisation (Aco)mentioning

confidence: 99%

Graphics processing units and genetic programming: an overview

Langdon

2011

Soft Comput

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A top end graphics card (GPU) plus a suitable SIMD interpreter, can deliver a several hundred fold speed up, yet cost less than the computer holding it. We give highlights of AI and computational intelligence applications in the new field of general purpose computing on graphics hardware (GPGPU). In particular we survey genetic programming (GP) use with GPU. We give several applications from Bioinformatics and show how the fastest GP is based on an interpreter rather than compilation. Finally using GP to generate GPU CUDA kernel C++ code is sketched.

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Section: Ant Colony Optimisation (Aco)mentioning

confidence: 99%

Graphics processing units and genetic programming: an overview

Langdon

2011

Soft Comput

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“…The colonies can cooperate by periodically exchanging information [71]. On a single GPU this approach can be realized by assigning one colony per block, as done by Bai et al in [5] and by Delévacq et al in [25]. If several GPUs are available, one can of course use one GPU per colony as studied by Delévacq et al in [25].…”

Section: Authormentioning

confidence: 99%

“…Thus we have the one-ant-per-thread and the one-ant-per-block schemes. Many papers implement either the former (Bai et al [5], You [96], Diego et al [27]) or the latter (Li et al [51], Uchida et al [89]). Only a few publications (Cecilia et al [14], Delévacq et al [25]) compare the two.…”

Section: Authormentioning

confidence: 99%

“…Year Problem Algorithm GPU(s) Catala et al [13] 2007 OP ACO GeForce 6600 GT Bai et al [5] 2009 TSP multi colony MMAS GeForce 8800 GTX Li et al [51] 2009 TSP MMAS GeForce 8600 GT Wang et al [90] 2009 TSP MMAS Quadro Fx 4500 You [96] 2009 TSP ACO Tesla C1060 Cecilia et al [14] 2011 TSP ACO Tesla C2050 Delévacq et al [25] With the arrival of CUDA and OpenCL, programming the GPU became easier and consequently more papers studied ACO implementations on the GPU. In CUDA and OpenCL there is the basic concept of having a thread/workitem as basic computational element.…”

Section: Authormentioning

confidence: 99%

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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, these architectures offer a substantial computational horsepower and a high memory bandwidth compared to CPU-based architectures. Due to their inherent parallel nature, P-metaheuristics such as evolutionary algorithms have been the first subject of parallelization on GPU: genetic algorithms [7], particle swarm optimization [8], ant colonies [9] and so on.…”

Section: Metaheuristics On Gpu Architecturesmentioning

confidence: 99%

Parallelization Strategies for Hybrid Metaheuristics Using a Single GPU and Multi-core Resources

Taillard

Melab

Talbi

2012

Lecture Notes in Computer Science

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Abstract. Hybrid metaheuristics are powerful methods for solving complex problems in science and industry. Nevertheless, the resolution time remains prohibitive when dealing with large problem instances. As a result, the use of GPU computing has been recognized as a major way to speed up the search process. However, most GPU-accelerated algorithms of the literature do not take benefits of all the available CPU cores. In this paper, we introduce a new guideline for the design and implementation of effective hybrid metaheuristics using heterogeneous resources.

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MAX-MIN Ant System on GPU with CUDA

Cited by 43 publications

References 11 publications

Graphics processing units and genetic programming: an overview

Graphics processing units and genetic programming: an overview

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

Parallelization Strategies for Hybrid Metaheuristics Using a Single GPU and Multi-core Resources

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