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GPU-accelerated high-performance encoding and decoding of hierarchical RAID in virtual machines

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Abstract

This paper proposes new GPU-accelerated high-performance encoding and decoding for hierarchical RAID in a multiple virtual machine environment. Pass-through GPU technology is used to provide dedicated access to GPU cores for each virtual machine, and for a virtual desktop, it also enables higher encoding and decoding performance than traditional vGPU technology. The proposed hierarchical RAID reduces the GPU overhead and resists node failure. Experimental results show that the average encoding performance of the proposed hierarchical RAID 55 improves by 11.03%, compared to another hierarchical RAID 51, with respect to various file sizes. In addition, the average disk-based decoding performance of the proposed hierarchical RAID 55 also improves by 59.61%.

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Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A01061112).

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Authors and Affiliations

  1. Department of Electronic Engineering, Inha University, Incheon, South Korea

    Tae-Geon Song, Mehdi Pirahandeh, Cheong-Jin Ahn & Deok-Hwan Kim

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  1. Tae-Geon Song
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  2. Mehdi Pirahandeh
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  3. Cheong-Jin Ahn
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  4. Deok-Hwan Kim
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Correspondence to Deok-Hwan Kim.

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Song, TG., Pirahandeh, M., Ahn, CJ. et al. GPU-accelerated high-performance encoding and decoding of hierarchical RAID in virtual machines. J Supercomput 74, 5865–5888 (2018). https://doi.org/10.1007/s11227-017-1969-y

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