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ValuePack: value-based scheduling framework for CPU-GPU clusters

Vignesh T. Ravi, Michela Becchi, Gagan Agrawal, Srimat Chakradhar
Dept. of Computer Science and Engg., The Ohio State University, Columbus, Ohio
International Conference on High Performance Computing, Networking, Storage and Analysis (SC’12), 2012
@inproceedings{ravi2012valuepack,

   title={ValuePack: value-based scheduling framework for CPU-GPU clusters},

   author={Ravi, V.T. and Becchi, M. and Agrawal, G. and Chakradhar, S.},

   booktitle={Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis},

   pages={53},

   year={2012},

   organization={IEEE Computer Society Press}

}

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Heterogeneous computing nodes are becoming commonplace today, and recent trends strongly indicate that clusters, supercomputers, and cloud environments will increasingly host more heterogeneous resources, with some being massively parallel (e.g., GPU). With such heterogeneous environments becoming common, it is important to revisit scheduling problems for clusters and cloud environments. In this paper, we formulate and address the problem of value-driven scheduling of independent jobs on heterogeneous clusters, which captures both the urgency and relative priority of jobs. Our overall scheduling goal is to maximize the aggregate value or yield of all jobs. Exploiting the portability available from the underlying programming model, we propose four novel scheduling schemes that can automatically and dynamically map jobs onto heterogeneous resources. Additionally, to improve the utilization of massively parallel resources, we also propose heuristics to automatically decide when and which jobs can share a single resource.
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