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Virtualizing high-end GPGPUs on ARM clusters for the next generation of high performance cloud computing

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Abstract

High performance cloud computing is behind the scene powering “the next big thing” as the mainstream accelerator for innovation in many areas. We describe here how to accelerate inexpensive ARM-based computing nodes with high-end GPGPUs hosted on x86_64 machines using the GVirtuS general-purpose virtualization service. We draw the vision of a possible next generation computing clusters characterized by highly heterogeneous parallelism heading to a lower electric power demanding, less heat producing and more environmental friendliness. Preliminary but promising performance data suggest that this solution could be considered as part of the foundations of the next generation of high performance cloud computing components.

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Acknowledgments

This work was supported by the fund for internal reseach projects of the University Parthenope of Napoli and by the SPACI/CNR project managed by the Department of Science and Technology—High Performance Scientific Computing Laborarty at UniParthenope (LMNCP, http://lmncp.uniparthenope.it). Portions of this effort were conducted within the Campania Region Marine and Atmosphere Monitoring and Modelling Centre (CCMMMA, http://meteo.uniparthenope.it). All the code produced under this research are or will be released as open source with GPL/LGPL license.

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Authors and Affiliations

  1. Department of Science and Technology, University of Napoli Parthenope, CDN Isola C4, 80143 , Naples, Italy

    Raffaele Montella & Giulio Giunta

  2. Department of Mathematics and Applications, University of Napoli Federico II, Via Cintia, Complesso Monte S. Angelo, 26, 80125 , Naples, Italy

    Giuliano Laccetti

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  1. Raffaele Montella
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  2. Giulio Giunta
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  3. Giuliano Laccetti
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Correspondence to Raffaele Montella.

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Montella, R., Giunta, G. & Laccetti, G. Virtualizing high-end GPGPUs on ARM clusters for the next generation of high performance cloud computing. Cluster Comput 17, 139–152 (2014). https://doi.org/10.1007/s10586-013-0341-0

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