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Run-time performance and power optimization of parallel disparity estimation on many-core platforms

Run-time performance and power optimization of parallel disparity estimation on many-core platforms
Run-time performance and power optimization of parallel disparity estimation on many-core platforms
This paper investigates the use of many-core systems to execute the disparity estimation algorithm, used in stereo vision applications, as these systems can provide flexibility between performance scaling and power consumption. We present a learning-based run-time management approach which achieves a required performance threshold whilst minimizing power consumption through dynamic control of frequency and core allocation. Experimental results are obtained from a 61-core Intel Xeon Phi platform for the above investigation. The same performance can be achieved with an average reduction in power consumption of 27.8% and increased energy efficiency by 30.04% when compared to DVFS control alone without run-time management.
1539-9087
Leech, Charles
6ba70c54-3792-41cd-a8d6-9e8884ae004f
Vala, Charan Kumar
41279fa4-5cb5-469b-88ae-cc43a35c4325
Acharyya, Amit
1f8a0620-1c00-4306-a64c-5185ede71f38
Yang, Sheng
04b9848f-ddd4-4d8f-93b6-b91a2144d49c
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Leech, Charles
6ba70c54-3792-41cd-a8d6-9e8884ae004f
Vala, Charan Kumar
41279fa4-5cb5-469b-88ae-cc43a35c4325
Acharyya, Amit
1f8a0620-1c00-4306-a64c-5185ede71f38
Yang, Sheng
04b9848f-ddd4-4d8f-93b6-b91a2144d49c
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d

Leech, Charles, Vala, Charan Kumar, Acharyya, Amit, Yang, Sheng, Merrett, Geoffrey and Al-Hashimi, Bashir (2017) Run-time performance and power optimization of parallel disparity estimation on many-core platforms. ACM Transactions on Embedded Computing Systems. (In Press)

Record type: Article

Abstract

This paper investigates the use of many-core systems to execute the disparity estimation algorithm, used in stereo vision applications, as these systems can provide flexibility between performance scaling and power consumption. We present a learning-based run-time management approach which achieves a required performance threshold whilst minimizing power consumption through dynamic control of frequency and core allocation. Experimental results are obtained from a 61-core Intel Xeon Phi platform for the above investigation. The same performance can be achieved with an average reduction in power consumption of 27.8% and increased energy efficiency by 30.04% when compared to DVFS control alone without run-time management.

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Accepted/In Press date: 11 August 2017

Identifiers

Local EPrints ID: 413390
URI: https://eprints.soton.ac.uk/id/eprint/413390
ISSN: 1539-9087
PURE UUID: 8faa1ae1-d311-4db8-9989-a66eff9d1854
ORCID for Charles Leech: ORCID iD orcid.org/0000-0002-2403-3873
ORCID for Geoffrey Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 23 Aug 2017 16:31
Last modified: 06 Jun 2018 12:42

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