GPGPU Task Management Technique to Mitigate Performance Degradation of Virtual Machines due to GPU Operation in Cloud Environments


KIPS Transactions on Computer and Communication Systems, Vol. 9, No. 9, pp. 189-196, Sep. 2020
https://doi.org/10.3745/KTCCS.2020.9.9.189,   PDF Download:
Keywords: GPU Virtualization, Performance isolation, Scheduling, Cloud, GPGPU Task Management
Abstract

Recently, GPU cloud computing technology applying GPU(Graphics Processing Unit) devices to virtual machines is widely used in the cloud environment. In a cloud environment, GPU devices assigned to virtual machines can perform operations faster than CPUs through massively parallel processing, which can provide many benefits when operating high-performance computing services in a variety of fields in a cloud environment. In a cloud environment, a GPU device can help improve the performance of a virtual machine, but the virtual machine scheduler, which is based on the CPU usage time of a virtual machine, does not take into account GPU device usage time, affecting the performance of other virtual machines. In this paper, we test and analyze the performance degradation of other virtual machines due to the virtual machine that performs GPGPU(General-Purpose computing on Graphics Processing Units) task in the direct path based GPU virtualization environment, which is often used when assigning GPUs to virtual machines in cloud environments. Then to solve this problem, we propose a GPGPU task management method for a virtual machine.


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Cite this article
[IEEE Style]
J. Kang and J. Gil, "GPGPU Task Management Technique to Mitigate Performance Degradation of Virtual Machines due to GPU Operation in Cloud Environments," KIPS Transactions on Computer and Communication Systems, vol. 9, no. 9, pp. 189-196, 2020. DOI: https://doi.org/10.3745/KTCCS.2020.9.9.189.

[ACM Style]
Jihun Kang and Joon-Min Gil. 2020. GPGPU Task Management Technique to Mitigate Performance Degradation of Virtual Machines due to GPU Operation in Cloud Environments. KIPS Transactions on Computer and Communication Systems, 9, 9, (2020), 189-196. DOI: https://doi.org/10.3745/KTCCS.2020.9.9.189.