GPU Resource Contention Management Technique for Simultaneous GPU Tasks in the Container Environments with Share the GPU


KIPS Transactions on Computer and Communication Systems, Vol. 11, No. 10, pp. 333-344, Oct. 2022
https://doi.org/10.3745/KTCCS.2022.11.10.333,   PDF Download:  
Keywords: HPC Cloud, Container, GPU Computing, GPU Sharing, Resource Race
Abstract

In a container-based cloud environment, multiple containers can share a graphical processing unit (GPU), and GPU sharing can minimize idle time of GPU resources and improve resource utilization. However, in a cloud environment, GPUs, unlike CPU or memory, cannot logically multiplex computing resources to provide users with some of the resources in an isolated form. In addition, containers occupy GPU resources only when performing GPU operations, and resource usage is also unknown because the timing or size of each container's GPU operations is not known in advance. Containers unrestricted use of GPU resources at any given point in time makes managing resource contention very difficult owing to where multiple containers run GPU tasks simultaneously, and GPU tasks are handled in black box form inside the GPU. In this paper, we propose a container management technique to prevent performance degradation caused by resource competition when multiple containers execute GPU tasks simultaneously. Also, this paper demonstrates the efficiency of container management techniques that analyze and propose the problem of degradation due to resource competition when multiple containers execute GPU tasks simultaneously through experiments.


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Cite this article
[IEEE Style]
J. Kang, "GPU Resource Contention Management Technique for Simultaneous GPU Tasks in the Container Environments with Share the GPU," KIPS Transactions on Computer and Communication Systems, vol. 11, no. 10, pp. 333-344, 2022. DOI: https://doi.org/10.3745/KTCCS.2022.11.10.333.

[ACM Style]
Jihun Kang. 2022. GPU Resource Contention Management Technique for Simultaneous GPU Tasks in the Container Environments with Share the GPU. KIPS Transactions on Computer and Communication Systems, 11, 10, (2022), 333-344. DOI: https://doi.org/10.3745/KTCCS.2022.11.10.333.