PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems
Future many-core systems need to address the dark silicon problem, where some cores would be turned off to control the chip’s thermal and power density, which effectively limits the performance gain from having a large number of processing cores. Task migration technique has been previous...
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| Format: | Article |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9527255/ |
| _version_ | 1818661692693807104 |
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| author | Mohammed Sultan Mohammed Norlina Paraman Ab Al-Hadi Ab Rahman Fuad A. Ghaleb Ahlam Al-Dhamari Muhammad Nadzir Marsono |
| author_facet | Mohammed Sultan Mohammed Norlina Paraman Ab Al-Hadi Ab Rahman Fuad A. Ghaleb Ahlam Al-Dhamari Muhammad Nadzir Marsono |
| author_sort | Mohammed Sultan Mohammed |
| collection | DOAJ |
| description | Future many-core systems need to address the dark silicon problem, where some cores would be turned off to control the chip’s thermal and power density, which effectively limits the performance gain from having a large number of processing cores. Task migration technique has been previously proposed to improve many-core system performance by moving tasks between active and dark cores. As task migration imposes system performance overhead due to the large wake-up latency of the dark cores, this paper proposes a prediction-based early wake-up (PEW) to reduce the dark cores’ wake-up latency during task migration. A window-based online ridge regression (RR) is used as the prediction model. The prediction model uses the past window’s thermal, power, and core status (i.e., active or dark) to predict the future core temperatures at run-time. If task migration is predicted in the next control period, the proposed PEW puts the dark cores in a power state with low wake-up latency. Thus, the proposed PEW reduces the time for the dark cores to start executing the tasks. The comparison results show that our proposed PEW reduces the completion time by up to 7.9% and 4.1% compared to non-early wake-up (NoEW) and a fixed threshold wake-up (FEW), respectively. It also shows that the proposed PEW increases the MIPS/Watt by up to 5.5% and 2.3% over NoEW and FEW, respectively. These results show that the proposed PEW improves the many-core system’s overall performance in terms of reducing dark cores’ wake-up latency and increasing the number of executed instructions per Watt. |
| first_indexed | 2024-12-17T04:49:06Z |
| format | Article |
| id | doaj.art-2bf7e79c114643dc8cf9167eba92ca1f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T04:49:06Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2bf7e79c114643dc8cf9167eba92ca1f2022-12-21T22:02:58ZengIEEEIEEE Access2169-35362021-01-01912408712409910.1109/ACCESS.2021.31097179527255PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core SystemsMohammed Sultan Mohammed0https://orcid.org/0000-0002-2702-6206Norlina Paraman1https://orcid.org/0000-0001-7615-7432Ab Al-Hadi Ab Rahman2https://orcid.org/0000-0002-0754-5199Fuad A. Ghaleb3https://orcid.org/0000-0002-1468-0655Ahlam Al-Dhamari4https://orcid.org/0000-0001-7595-4632Muhammad Nadzir Marsono5https://orcid.org/0000-0002-7468-7461School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, MalaysiaSchool of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, MalaysiaSchool of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, MalaysiaSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, MalaysiaSchool of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, MalaysiaSchool of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, MalaysiaFuture many-core systems need to address the dark silicon problem, where some cores would be turned off to control the chip’s thermal and power density, which effectively limits the performance gain from having a large number of processing cores. Task migration technique has been previously proposed to improve many-core system performance by moving tasks between active and dark cores. As task migration imposes system performance overhead due to the large wake-up latency of the dark cores, this paper proposes a prediction-based early wake-up (PEW) to reduce the dark cores’ wake-up latency during task migration. A window-based online ridge regression (RR) is used as the prediction model. The prediction model uses the past window’s thermal, power, and core status (i.e., active or dark) to predict the future core temperatures at run-time. If task migration is predicted in the next control period, the proposed PEW puts the dark cores in a power state with low wake-up latency. Thus, the proposed PEW reduces the time for the dark cores to start executing the tasks. The comparison results show that our proposed PEW reduces the completion time by up to 7.9% and 4.1% compared to non-early wake-up (NoEW) and a fixed threshold wake-up (FEW), respectively. It also shows that the proposed PEW increases the MIPS/Watt by up to 5.5% and 2.3% over NoEW and FEW, respectively. These results show that the proposed PEW improves the many-core system’s overall performance in terms of reducing dark cores’ wake-up latency and increasing the number of executed instructions per Watt.https://ieeexplore.ieee.org/document/9527255/Dark siliconmany-core systemstask migrationdynamic voltage frequency scaling (DVFS)ridge regressionearly wake-up |
| spellingShingle | Mohammed Sultan Mohammed Norlina Paraman Ab Al-Hadi Ab Rahman Fuad A. Ghaleb Ahlam Al-Dhamari Muhammad Nadzir Marsono PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems IEEE Access Dark silicon many-core systems task migration dynamic voltage frequency scaling (DVFS) ridge regression early wake-up |
| title | PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems |
| title_full | PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems |
| title_fullStr | PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems |
| title_full_unstemmed | PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems |
| title_short | PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems |
| title_sort | pew prediction based early dark cores wake up using online ridge regression for many core systems |
| topic | Dark silicon many-core systems task migration dynamic voltage frequency scaling (DVFS) ridge regression early wake-up |
| url | https://ieeexplore.ieee.org/document/9527255/ |
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