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

Taillard, Éric D.; Melab, Nouredine; Talbi, El‐Ghazali

doi:10.1007/978-3-642-32964-7_37

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Luong et al propose in [63] a load balancing scheme to distribute multiple metaheuristics over both the GPU and the CPU cores simultaneously. They apply the scheme to the quadratic assignment problem using the fast ant metaheuristic, yielding a combined speedup (both multiple cores on CPU and GPU) of up to 15.8 compared to a single core on the CPU.…”

Section: Hybrid Metaheuristicsmentioning

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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Section: Hybrid Metaheuristicsmentioning

confidence: 99%