Implementation of Clock Settings and Scaling Governor on ARM big.LITTLE Central Processing Unit (CPU)
DOI:
https://doi.org/10.22236/ate.v3i1.12037Keywords:
Central Processing Unit, Scaling Governor, ARM Big.LITTLE, BenchmarkingAbstract
CPU is an important component in handling the performance and battery power efficiency of smartphones. In CPUs with ARM big.LITTLE architecture, there is a scaling governor system to determine the ups and downs of the CPU frequency according to workload demands. Experimental testing of the selection of the right scaling governor according to the workload and clock settings on the ARM big.LITTLE CPU to a fixed frequency, has been carried out as a special step in overcoming user problems. Implementation was done on a Poco X3 with Snapdragon 732G SoC, CPU clock ranging from 300-2300 MHz and built-in schedutil scaling governor. The clock interval chosen is the top 2300 MHz with performance scaling, the middle 1324 MHz with userspace scaling, and the bottom 300 MHz with powersafe scaling. Experimental results from an average of 20 trials show, the higher the CPU clock, the performance will also increase, this is directly proportional to the temperature, and battery power usage both when the smartphone is idle without load and full load with load.
Downloads
References
A. Asri, “Implementasi dan Analisis Overcloking Pada Prosessor AMD RYZEN 5 2600 Terhadap Kinerja Sistem Komputer,” UNIVERSITAS ISLAM NEGERI AR-RANIRY, BANDA ACEH, 2020.
P.-K. Chuang, Y.-S. Chen, and P.-H. Huang, “An adaptive on-line CPU-GPU governor for games on mobile devices,” in 2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC), Chiba, Japan: IEEE, Jan. 2017, pp. 653–658. doi: 10.1109/ASPDAC.2017.7858398.
T. Benoit-Cattin, D. Velasco-Montero, and J. Fernández-Berni, “Impact of Thermal Throttling on Long-Term Visual Inference in a CPU-Based Edge Device,” Electronics, vol. 9, no. 12, p. 2106, Dec. 2020, doi: 10.3390/electronics9122106.
R. Yadav and R. S. Bhadoria, “Performance Analysis for Android Runtime Environment,” in 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, India: IEEE, Apr. 2015, pp. 1076–1079. doi: 10.1109/CSNT.2015.52.
E. Vasilakis, I. Sourdis, V. Papaefstathiou, A. Psathakis, and M. G. H. Katevenis, “Modeling energy-performance tradeoffs in ARM big.LITTLE architectures,” in 2017 27th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS), Thessaloniki: IEEE, Sep. 2017, pp. 1–8. doi: 10.1109/PATMOS.2017.8106950.
Y. Zhang, Y. Liu, L. Zhuang, X. Liu, F. Zhao, and Q. Li, “Accurate CPU Power Modeling for Multicore Smartphones”.
Z. Bringye, D. Sima, and M. Kozlovszky, “Power consumption aware big.LITTLE scheduler for Linux operating system,” in 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), Budapest, Hungary: IEEE, Jul. 2019, pp. 000139–000144. doi: 10.1109/IWOBI47054.2019.9114403.
E. Ahmad and B. Shihada, “Green smartphone GPUs: Optimizing energy consumption using GPUFreq scaling governors,” in 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Abu Dhabi, United Arab Emirates: IEEE, Oct. 2015, pp. 740–747. doi: 10.1109/WiMOB.2015.7348036.
Albertus Alva Putu Suwata, “Analisis Unjuk Kerja Sumber Daya Hardware Smartphone Android Pada WIFI Tethering Bedasarkan Operating System,” Universitas Sanata Dharma, YOGYAKARTA, 2016.
M. Rahardi and M. Bagaskara, “Analisis Kinerja Overclocking CPU dan GPU Terhadap Kecepatan Rendering Project 3D,” J.InfoMedia, vol. 7, no. 2, p. 82, Dec. 2022, doi: 10.30811/jim.v7i2.3360.
Yunanri. W. Muhammad Abdul Aziz, “Analisis Performance Central Prosessing Unit (CPU) Realtime Menggunakan Metode Benchmarking,” matrik, vol. 20, no. 2, pp. 237–248, Mei 22.
A. FIRDAUS H, “Analisis Kinerja Prosessor Terhadap Proses Overcloking dan Downcloking,” Universitas Muhammdiyah Makasar, MAKASAR, 2016.
T. Yusnanto, M. A. Machmudi, and K. Mustofa, “Pengaruh Overclocking Prosessor Intel CORE 2 DUO E8400 Pada Motherboard ASUS P5Q Dan ASUS P5P43TD,” JT, vol. 17, no. 1, Jun. 2021, doi: 10.56357/jt.v17i1.257.
P. Katemba and R. K. Djoh, “PREDIKSI TINGKAT PRODUKSI KOPI MENGGUNAKAN REGRESI LINEAR,” Jurnal Ilmiah FLASH, vol. 3, no. 1, pp. 42–51, Jun. 2017.
.png)
