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Energy efficient run-time mapping and thread partitioning of concurrent OpenCL applications on CPU-GPU MPSoCs

Energy efficient run-time mapping and thread partitioning of concurrent OpenCL applications on CPU-GPU MPSoCs
Energy efficient run-time mapping and thread partitioning of concurrent OpenCL applications on CPU-GPU MPSoCs
Heterogeneous Multi-Processor Systems-on-Chips (MPSoCs) containing CPU and GPU cores are typically required to execute applications concurrently. However, as will be shown in this paper, existing approaches are not well suited for concurrent applications as they are developed either by considering only a single application or they do not exploit both CPU and GPU cores at the same time. In this paper, we propose an energy-efficient run-time mapping and thread partitioning approach for executing concurrent OpenCL applications on both GPU and GPU cores while satisfying performance requirements. Depending upon the performance requirements, for each concurrently executing application, the mapping process finds the appropriate number of CPU cores and operating frequencies of CPU and GPU cores, and the partitioning process identifies an efficient partitioning of the applications’ threads between CPU and GPU cores. We validate the proposed approach experimentally on the Odroid-XU3 hardware platform with various mixes of applications from the Polybench benchmark suite. Additionally, a case-study is performed with a real-world application SLAMBench. Results show an average energy saving of 32% compared to existing approaches while still satisfying the performance requirements.
Heterogeneous MPSoC, OpenCL applications, Run-time management, Performance, Energy consumption
1539-9087
Singh, Amit
bb67d43e-34d9-4b58-9295-8b5458270408
Prakash, Alok
f2617935-6771-4ad7-97a8-360e608fd097
Basireddy, Karunakar Reddy
5bfb0b2e-8242-499a-a52b-e813d9a90889
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Singh, Amit
bb67d43e-34d9-4b58-9295-8b5458270408
Prakash, Alok
f2617935-6771-4ad7-97a8-360e608fd097
Basireddy, Karunakar Reddy
5bfb0b2e-8242-499a-a52b-e813d9a90889
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d

Singh, Amit, Prakash, Alok, Basireddy, Karunakar Reddy, Merrett, Geoffrey and Al-Hashimi, Bashir (2017) Energy efficient run-time mapping and thread partitioning of concurrent OpenCL applications on CPU-GPU MPSoCs ACM Transactions on Embedded Computing Systems (doi:10.1145/3126548).

Record type: Article

Abstract

Heterogeneous Multi-Processor Systems-on-Chips (MPSoCs) containing CPU and GPU cores are typically required to execute applications concurrently. However, as will be shown in this paper, existing approaches are not well suited for concurrent applications as they are developed either by considering only a single application or they do not exploit both CPU and GPU cores at the same time. In this paper, we propose an energy-efficient run-time mapping and thread partitioning approach for executing concurrent OpenCL applications on both GPU and GPU cores while satisfying performance requirements. Depending upon the performance requirements, for each concurrently executing application, the mapping process finds the appropriate number of CPU cores and operating frequencies of CPU and GPU cores, and the partitioning process identifies an efficient partitioning of the applications’ threads between CPU and GPU cores. We validate the proposed approach experimentally on the Odroid-XU3 hardware platform with various mixes of applications from the Polybench benchmark suite. Additionally, a case-study is performed with a real-world application SLAMBench. Results show an average energy saving of 32% compared to existing approaches while still satisfying the performance requirements.

Text 59_Singh - Accepted Manuscript
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More information

Accepted/In Press date: 30 June 2017
e-pub ahead of print date: 10 October 2017
Keywords: Heterogeneous MPSoC, OpenCL applications, Run-time management, Performance, Energy consumption

Identifiers

Local EPrints ID: 412822
URI: http://eprints.soton.ac.uk/id/eprint/412822
ISSN: 1539-9087
PURE UUID: a31f7c0e-03ff-4e24-a73c-5b3f602aef91
ORCID for Karunakar Reddy Basireddy: ORCID iD orcid.org/0000-0001-9755-1041
ORCID for Geoffrey Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 02 Aug 2017 16:30
Last modified: 01 Nov 2017 17:32

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Contributors

Author: Amit Singh
Author: Alok Prakash
Author: Karunakar Reddy Basireddy ORCID iD
Author: Geoffrey Merrett ORCID iD

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