Heterogeneous GPU reallocation


James Gleeson and Eyal de Lara, University of Toronto


Emerging cloud markets like spot markets and batch computing services scale up services at the granularity of whole VMs. In this paper, we observe that GPU workloads underutilize GPU device memory, leading us to explore the benefits of reallocating heterogeneous GPUs within existing VMs. We outline approaches for upgrading and downgrading GPUs for OpenCL GPGPU workloads, and show how to minimize the chance of cloud operator VM termination by maximizing the heterogeneous environments in which applications can run.

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@inproceedings {203306,
author = {James Gleeson and Eyal de Lara},
title = {Heterogeneous {GPU} reallocation},
booktitle = {9th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 17)},
year = {2017},
address = {Santa Clara, CA},
url = {https://www.usenix.org/conference/hotcloud17/program/presentation/gleeson},
publisher = {USENIX Association},
month = jul