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gRemote: Cloud rendering on GPU resource pool based on API-forwarding

delete2021-06-01
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PRE
AI
D
Dongjie Tang
L
Linsheng Li
J
Jiacheng Ma
Xue Liu 封面图
Xue Liu (Xue Liu)
戚
戚正伟 (Zhengwei Qi) *
H
Haibing Guan
DOI:10.1016/j.sysarc.2021.102055delete
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摘要

摘要

En 中文
Traditional GPU resource allocation approaches, widely adopted in today's data centers as the rise of cloud gaming, only focus on server-side functions while ignoring the client-side, which wastes hardware resources. To solve this problem, cloud-edge integrated architectures are put forward, leaving some workloads to the clientside. However, many cloud-edge integrated frameworks suffer from one big issue: shared-resource interference, stemming from two reasons: (a) GPU resource racing caused by resources overuse for single client, and (b) CPU resource racing caused by resources shortage among clients. This paper presents gRemote, an open-source cloud-rendering system that can address these issues on a GPU resource pool. To mitigate the CPU resource shortage, gRemote improves CPU configuration by expanding CPU resources from server-side to both server- and client-side. To maintain the reasonable GPU usage for individual tasks, gRemote innovates a new resource-sharing mechanism called GPU throttle. Furthermore, remote API-Forwarding brings another type of network bandwidth consumption: command streaming. To optimize the network bandwidth for GPU command transmission, gRemote proposes two methods, command characteristic-soriented (CCO) compression algorithm and command-transmitted methodology. gRemote supports 1,228 OpenGL commands and provides cloud rendering for more than 40 clients with negligible shared-resource interference. With CCO algorithm and command-transmitted methodology, the network bandwidth is saved by more than 90%.
Keyword:
Cloud rendering
API-forwarding
OpenGL
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期刊

Journal of Systems Architecture 封面图
Journal of Systems Architecture
IF:
4.1
论文数:
3.0K
被引数:
4.2K

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shanghai jiao tong university
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University of Michigan
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university of michigan system
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