Fine-grained task-level parallel and low power H.264 decoding in multi-core systems
In the past few years, the extinction of Moore's Law makes people reconsider the solutions for dealing with the low computing resource utilization of applications on multicore processor systems. However, making good use of computing resources in multi-core processors systems is not easy due to...
| Main Authors: | , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/144759 |
| _version_ | 1811681066615832576 |
|---|---|
| author | Liu, Wenyang Liu, Weichen Li, Mengquan Chen, Peng Yang, Lei Xiao, Chunhua Ye, Yaoyao |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Liu, Wenyang Liu, Weichen Li, Mengquan Chen, Peng Yang, Lei Xiao, Chunhua Ye, Yaoyao |
| author_sort | Liu, Wenyang |
| collection | NTU |
| description | In the past few years, the extinction of Moore's Law makes people reconsider the solutions for dealing with the low computing resource utilization of applications on multicore processor systems. However, making good use of computing resources in multi-core processors systems is not easy due to the differences between single-core and multi-core architecture. Nowadays short video apps like Instagram and Tik Tok have successfully caught people's eyes by fascinating short videos, typically just 10 to 30 seconds long, uploaded by the users of apps. And almost all of these videos are recorded by their mobile devices, which are typically HD (High Definition) or FHD (Full High Definition) videos, which prefer to be encoded/decoded by H.264/AVC rather then HEVC (High Efficiency Video Coding) on mobile devices in view of the energy consumption and decoding speed. How to dive the huge potential of the computing resource on multi-core mobile devices to speed up decoding these videos while consuming low energy, is a big challenge. In our previous work [1], a relatively simple parallel framework was proposed to implement a parallel H.264/ AV C decoder. This work further proposes a more detailed systematic task-level parallel framework, together with an energy saving strategy based on this framework, to research a new H.264/AVC decoder on multi-core processor systems. The proposed parallel method is composed of a set of rules to guide parallel software programming (PSPR) and a software parallelization framework (SPF). The PSPR is applied in pre-processing steps to address the potential issues limiting the inherent parallelism, and the SPF is applied to parallelize the original serial programs. After the parallelization is successfully deployed, DVFS technique would be applied to decrease the power dissipation based on the SPF. Results show that proposed solutions make a significant improvement in decoding speed of 32% at 720p, 27% at 1080p and 29% at 2160p, and in energy savings o... |
| first_indexed | 2024-10-01T03:35:02Z |
| format | Conference Paper |
| id | ntu-10356/144759 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:35:02Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1447592020-11-25T08:41:53Z Fine-grained task-level parallel and low power H.264 decoding in multi-core systems Liu, Wenyang Liu, Weichen Li, Mengquan Chen, Peng Yang, Lei Xiao, Chunhua Ye, Yaoyao School of Computer Science and Engineering 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) Engineering::Computer science and engineering Task-level Parallelization H.264 Decoding In the past few years, the extinction of Moore's Law makes people reconsider the solutions for dealing with the low computing resource utilization of applications on multicore processor systems. However, making good use of computing resources in multi-core processors systems is not easy due to the differences between single-core and multi-core architecture. Nowadays short video apps like Instagram and Tik Tok have successfully caught people's eyes by fascinating short videos, typically just 10 to 30 seconds long, uploaded by the users of apps. And almost all of these videos are recorded by their mobile devices, which are typically HD (High Definition) or FHD (Full High Definition) videos, which prefer to be encoded/decoded by H.264/AVC rather then HEVC (High Efficiency Video Coding) on mobile devices in view of the energy consumption and decoding speed. How to dive the huge potential of the computing resource on multi-core mobile devices to speed up decoding these videos while consuming low energy, is a big challenge. In our previous work [1], a relatively simple parallel framework was proposed to implement a parallel H.264/ AV C decoder. This work further proposes a more detailed systematic task-level parallel framework, together with an energy saving strategy based on this framework, to research a new H.264/AVC decoder on multi-core processor systems. The proposed parallel method is composed of a set of rules to guide parallel software programming (PSPR) and a software parallelization framework (SPF). The PSPR is applied in pre-processing steps to address the potential issues limiting the inherent parallelism, and the SPF is applied to parallelize the original serial programs. After the parallelization is successfully deployed, DVFS technique would be applied to decrease the power dissipation based on the SPF. Results show that proposed solutions make a significant improvement in decoding speed of 32% at 720p, 27% at 1080p and 29% at 2160p, and in energy savings o... Nanyang Technological University This work is partially supported by NAP M4082282 and SUG M4082087 from NTU Singapore, and NSFC 61772094, Chongqing High-Tech Program cstc2017jcyjA1430, the Fundamental Research Funds for the Central Universities 106112017CDJQJ188829, China, and China Scholarship Council No. 201706050117. 2020-11-23T08:29:50Z 2020-11-23T08:29:50Z 2019 Conference Paper Liu, W., Liu, W., Li, M., Chen, P., Yang, L., Xiao, C., & Ye, Y. (2018). Fine-grained task-level parallel and low power H.264 decoding in multi-core systems. Proceedings of 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS), 307-314. doi:10.1109/PADSW.2018.8644865 978-1-5386-7308-9 https://hdl.handle.net/10356/144759 10.1109/PADSW.2018.8644865 307 314 en NAP M4082282 SUG M4082087 © 2018 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Task-level Parallelization H.264 Decoding Liu, Wenyang Liu, Weichen Li, Mengquan Chen, Peng Yang, Lei Xiao, Chunhua Ye, Yaoyao Fine-grained task-level parallel and low power H.264 decoding in multi-core systems |
| title | Fine-grained task-level parallel and low power H.264 decoding in multi-core systems |
| title_full | Fine-grained task-level parallel and low power H.264 decoding in multi-core systems |
| title_fullStr | Fine-grained task-level parallel and low power H.264 decoding in multi-core systems |
| title_full_unstemmed | Fine-grained task-level parallel and low power H.264 decoding in multi-core systems |
| title_short | Fine-grained task-level parallel and low power H.264 decoding in multi-core systems |
| title_sort | fine grained task level parallel and low power h 264 decoding in multi core systems |
| topic | Engineering::Computer science and engineering Task-level Parallelization H.264 Decoding |
| url | https://hdl.handle.net/10356/144759 |
| work_keys_str_mv | AT liuwenyang finegrainedtasklevelparallelandlowpowerh264decodinginmulticoresystems AT liuweichen finegrainedtasklevelparallelandlowpowerh264decodinginmulticoresystems AT limengquan finegrainedtasklevelparallelandlowpowerh264decodinginmulticoresystems AT chenpeng finegrainedtasklevelparallelandlowpowerh264decodinginmulticoresystems AT yanglei finegrainedtasklevelparallelandlowpowerh264decodinginmulticoresystems AT xiaochunhua finegrainedtasklevelparallelandlowpowerh264decodinginmulticoresystems AT yeyaoyao finegrainedtasklevelparallelandlowpowerh264decodinginmulticoresystems |