Adaptive Simultaneous Multi-tenancy for GPUs

Autor:	Ramin Bashizade, Alvin R. Lebeck, Yuxuan Li
Rok vydání:	2019
Předmět:	020203 distributed computing Multitenancy Kernel (image processing) Computer science 0202 electrical engineering electronic engineering information engineering Workload 02 engineering and technology Parallel computing Graphics Massively parallel 020202 computer hardware & architecture
Zdroj:	Job Scheduling Strategies for Parallel Processing ISBN: 9783030106317 JSSPP
Popis:	Graphics Processing Units (GPUs) are energy-efficient massively parallel accelerators that are increasingly deployed in multi-tenant environments such as data-centers for general-purpose computing as well as graphics applications. Using GPUs in multi-tenant setups requires an efficient and low-overhead method for sharing the device among multiple users that improves system throughput while adapting to the changes in workload. This requires mechanisms to control the resources allocated to each kernel, and an efficient policy to make decisions about this allocation.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2347d5869a417707c37e117dfada6c57 https://doi.org/10.1007/978-3-030-10632-4_5 Zobrazit plný text záznamu