The University of Southampton
University of Southampton Institutional Repository

Collaborative adaptation for energy-efficient heterogeneous mobile SoCs

Collaborative adaptation for energy-efficient heterogeneous mobile SoCs
Collaborative adaptation for energy-efficient heterogeneous mobile SoCs
Heterogeneous Mobile System-on-Chips (SoCs) containing CPU and GPU cores are becoming prevalent in embedded computing, and they need to execute applications concurrently. However, existing run-time management approaches do not perform adaptive mapping and thread-partitioning of applications while exploiting both CPU and GPU cores at the same time. In this paper, we propose an adaptive mapping and thread-partitioning approach for energy-efficient execution of concurrent OpenCL applications on both CPU and GPU cores while satisfying performance requirements. To start execution of concurrent applications, the approach makes mapping (number of cores and operating frequencies) and partitioning (distribution of threads between CPU and GPU) decisions to satisfy performance requirements for each application. The mapping and partitioning decisions are made by having a collaboration between the CPU and GPU cores’ processing capabilities such that balanced execution can be performed. During execution, adaptation is triggered when new application(s) arrive, or an executing one finishes, that frees cores. The adaptation process identifies a new mapping and thread-partitioning in a similar collaborative manner for remaining applications provided it leads to an improvement in energy efficiency. The proposed approach is experimentally validated on the Odroid-XU3 hardware platform with varying set of applications. Results show an average energy saving of 37%, compared to existing approaches while satisfying the performance requirements.
SoC, concurrent execution, energy-efficiency, heterogeneous computing, adaptation
Singh, Amit Kumar
bded7886-24ab-4a24-8539-f8fe106426ac
Basireddy, Karunakar Reddy
5bfb0b2e-8242-499a-a52b-e813d9a90889
Prakash, Alok
f2617935-6771-4ad7-97a8-360e608fd097
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Singh, Amit Kumar
bded7886-24ab-4a24-8539-f8fe106426ac
Basireddy, Karunakar Reddy
5bfb0b2e-8242-499a-a52b-e813d9a90889
Prakash, Alok
f2617935-6771-4ad7-97a8-360e608fd097
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d

Singh, Amit Kumar, Basireddy, Karunakar Reddy, Prakash, Alok, Merrett, Geoff and Al-Hashimi, Bashir (2019) Collaborative adaptation for energy-efficient heterogeneous mobile SoCs. IEEE Transactions on Computers. (In Press)

Record type: Article

Abstract

Heterogeneous Mobile System-on-Chips (SoCs) containing CPU and GPU cores are becoming prevalent in embedded computing, and they need to execute applications concurrently. However, existing run-time management approaches do not perform adaptive mapping and thread-partitioning of applications while exploiting both CPU and GPU cores at the same time. In this paper, we propose an adaptive mapping and thread-partitioning approach for energy-efficient execution of concurrent OpenCL applications on both CPU and GPU cores while satisfying performance requirements. To start execution of concurrent applications, the approach makes mapping (number of cores and operating frequencies) and partitioning (distribution of threads between CPU and GPU) decisions to satisfy performance requirements for each application. The mapping and partitioning decisions are made by having a collaboration between the CPU and GPU cores’ processing capabilities such that balanced execution can be performed. During execution, adaptation is triggered when new application(s) arrive, or an executing one finishes, that frees cores. The adaptation process identifies a new mapping and thread-partitioning in a similar collaborative manner for remaining applications provided it leads to an improvement in energy efficiency. The proposed approach is experimentally validated on the Odroid-XU3 hardware platform with varying set of applications. Results show an average energy saving of 37%, compared to existing approaches while satisfying the performance requirements.

Text
CollaborativeAdaptationHeteroSoC - Accepted Manuscript
Download (1MB)

More information

Accepted/In Press date: 28 August 2019
Keywords: SoC, concurrent execution, energy-efficiency, heterogeneous computing, adaptation

Identifiers

Local EPrints ID: 434551
URI: https://eprints.soton.ac.uk/id/eprint/434551
PURE UUID: 1a94102f-6185-4d39-bd4b-1e69fb5ba7a8
ORCID for Karunakar Reddy Basireddy: ORCID iD orcid.org/0000-0001-9755-1041
ORCID for Geoff Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 01 Oct 2019 16:30
Last modified: 02 Oct 2019 00:35

Export record

Contributors

Author: Amit Kumar Singh
Author: Karunakar Reddy Basireddy ORCID iD
Author: Alok Prakash
Author: Geoff Merrett ORCID iD
Author: Bashir Al-Hashimi

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×