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 ex...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/10356/147719 |
| _version_ | 1826121701020139520 |
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| author | Singh, Amit Kumar Basireddy, Karunakar Reddy Prakash, Alok Merrett, Geoff V. Al-Hashimi, Bashir M. |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Singh, Amit Kumar Basireddy, Karunakar Reddy Prakash, Alok Merrett, Geoff V. Al-Hashimi, Bashir M. |
| author_sort | Singh, Amit Kumar |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:36:29Z |
| format | Journal Article |
| id | ntu-10356/147719 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:36:29Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1477192021-04-13T01:32:46Z Collaborative adaptation for energy-efficient heterogeneous mobile SoCs Singh, Amit Kumar Basireddy, Karunakar Reddy Prakash, Alok Merrett, Geoff V. Al-Hashimi, Bashir M. School of Computer Science and Engineering Engineering::Computer science and engineering Embedded Systems Message Systems 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. 2021-04-13T01:32:45Z 2021-04-13T01:32:45Z 2020 Journal Article Singh, A. K., Basireddy, K. R., Prakash, A., Merrett, G. V. & Al-Hashimi, B. M. (2020). Collaborative adaptation for energy-efficient heterogeneous mobile SoCs. IEEE Transactions On Computers, 69(2), 185-197. https://dx.doi.org/10.1109/TC.2019.2943855 1557-9956 0000-0003-2056-0569 0000-0001-9755-1041 0000-0001-8257-2974 0000-0003-4980-3894 https://hdl.handle.net/10356/147719 10.1109/TC.2019.2943855 2-s2.0-85078346190 2 69 185 197 en IEEE Transactions on Computers © 2020 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Embedded Systems Message Systems Singh, Amit Kumar Basireddy, Karunakar Reddy Prakash, Alok Merrett, Geoff V. Al-Hashimi, Bashir M. Collaborative adaptation for energy-efficient heterogeneous mobile SoCs |
| title | Collaborative adaptation for energy-efficient heterogeneous mobile SoCs |
| title_full | Collaborative adaptation for energy-efficient heterogeneous mobile SoCs |
| title_fullStr | Collaborative adaptation for energy-efficient heterogeneous mobile SoCs |
| title_full_unstemmed | Collaborative adaptation for energy-efficient heterogeneous mobile SoCs |
| title_short | Collaborative adaptation for energy-efficient heterogeneous mobile SoCs |
| title_sort | collaborative adaptation for energy efficient heterogeneous mobile socs |
| topic | Engineering::Computer science and engineering Embedded Systems Message Systems |
| url | https://hdl.handle.net/10356/147719 |
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