11956

Heterogeneous Computing and Grid Scheduling with Hierarchically Parallel Evolutionary Algorithms

Jinglian Wang, Bin Gong, Hong Liu, Shaohui Li, Juan Yi
School of Computer Science and Technology, Shandong University, Jinan 250101, China
Journal of Computational Information Systems 10: 8, 3291-3298, 2014

@article{wang2014heterogeneous,

   title={Heterogeneous Computing and Grid Scheduling with Hierarchically Parallel Evolutionary Algorithms},

   author={Wang, Jinglian and Gong, Bin and Liu, Hong and Li, Shaohui and Yi, Juan},

   year={2014}

}

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This work presents the novel parallel evolutionary algorithm (EA) for task scheduling in distributed heterogeneous computing and grid environments, NP-hard problems with capital relevance in distributed computing. Parallelization of the biologically inspired heuristics is hierarchically designed and integrates with the two traditional parallel models (master-slave models and island models). The method has been specifically implemented on the newly developed supercomputer platform of hybrid multi-core CPU+GPU using C-CUDA for solving large-sized realistic instances. Experiments are performed on both well-known problem instances and large instances that model medium-sized grid environments. The comparative study shows that the proposed parallel approach is able to achieve high solving efficacy, outperforming previous results reported in the related literature, and also showing a good scalability behavior when facing high dimension problem instances.
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