A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision
[article]
2022
arXiv
pre-print
Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU accelerators have been collectively constructed into a GPU datacenter. An efficient scheduler design for such GPU datacenter is crucially important to reduce the operational cost and improve resource utilization. However, traditional approaches designed for big data or high performance computing workloads can not support DL
arXiv:2205.11913v3
fatcat:fnbinueyijb4nc75fpzd6hzjgq