Analisis Performance Central Prosessing Unit (CPU) Realtime Menggunakan Metode Benchmarking
DOI:
https://doi.org/10.30812/matrik.v20i2.1142Keywords:
Analisis, Hardware, CPU, Benchmark, RealtimeAbstract
Perkembangan teknologi semakin berkembang cepat baik dari performa, grafik, bandwidth dan lain-lainnya sehingga mempengaruhi berbagai sendi kehidupan dan profesi, hal ini menyebabkan perubahan sistem pada piranti atau kinerja pada central prosessing unit. Pada dunia bisnis, saat ini telah memfaatkan kemajuan teknologi informasi demi kelancaran kerja dibidang yang digeluti baik sekala kecil maupun sekala besar. Metode yang digunakan benchmarking merupakan suatu proses mengidetifikasi terhadap hardware dan proses suatu tolak ukur sebuah performa yang diharapkan. Adapun langkah pengujian melakukan evalusi kinerja central prosessing unit (CPU) yang dilakukan pada kinerja hardware atau perangkat keras baik prosessor, ram, vega dan lain sebagainya. Hasil pengujian yang dilaksanakan pada cental prosessing unit (CPU) penggunaan ram oleh prosessor i3 sebesar 3.1 Gb, GPU 3%, Disk uses 1%, penggunaan network atau jaringan 7.7 Mbps, penggunaan power suplay very low. Prosessor i5 sebesar 4.2 Gb, GPU 0%, Disk uses 0%, penggunaan network atau jaringan 7.7 Mbps, penggunaan power suplay low. Prosessor i7 sebesar 2.5 Gb, GPU 9%, Disk uses 9%, penggunaan network atau jaringan 104 Kbps, penggunaan power suplay high.
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[6] S. Sidhanta, S. Mukhopadhyay, and W. Golab, “Dyn-YCSB : Benchmarking Adaptive Frameworks,†2019 IEEE World Congr. Serv., Vol. 2642–939X, PP. 392–393, 2019.
[7] Beau Potre, Brett R. Cowan, Edward DiBella, Sancgeetha Kulaseharan, Devavrat Likhite, Nils Noorman, Lennart Tautz, Nicholas Tustison, gert Wollny, Alistair A. Young, Avan Suinesiaputra. “An Open Benchmark Challenge for Motion Correction of myocardial Perfusion MRI,†IEEE. Vol. 21, No. 5, PP. 1–12, 2017.
[8] Rui Han, Lizy Kurian John, Jianfeng Zhan. “Benchmarking Big Data System: A Review†IEEE Transaction on Services Computing. Vol. 11, No. 3 , PP. 1-18, 2018.
[9] Peter Eckert, Auke J. Ijspeert “Benchmarking Agility For Multilegged Terrestrial Robots,â€IEEE Transactions On Robotics. Vol. 35, No. 2 , PP. 1-7, 2019.
[10] Sebastian Gallenm¨uller, Stephan G¨unther , Maurice Leclaire , Samuele Zoppi , Fabio Molinari Richard Sch¨offauer , Wolfgang Kellerer, Georg Carle “Benchmarking Networked Control Systems,†IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems. Vol. 1 , No. 2 , PP.7-12, 2018.
[11] Xuheng Duan, Haochen Pan, Lewis Tseng, Yingjian Wu..†BBB : Make Benchmarking Blockchains Configurable and Extensible" IEEE Pacific rim Internasional Symposium on Dependable Computing.Vol. 24 , No.1 , PP. 61 - 62, 2019.
[12] Raquel Almeida, Henrique Madeira “Evolving from Dependability to Resilience Benchmarks : Issues and Possibilities,â€IEEE Latin-American Symposium on Dependable Computing. Vol. 7 , No. 1 ,PP. 127–130, 2016.
[13] P. Studi, T. Elektro, F. Teknik, and U. Muhammadiyah, “Analisis Kinerja Prosesor terhadap Proses Overclocking dan Downclocking,†Vol. 1, No. 1, PP. 7–12, 2019.
[14] A. Karki, C. P. Keshava, and S. M. Shivakumar, “Tango : A Deep Neural Network Benchmark Suite for Various Accelerators,â€IEEE Int. Symp. Perform. Anal. Syst. Software.,Vol. , No. , PP. 137–138, 2019.
[15] Lin Li, Alfredo Alan Flores Saldivar, Yun Bai, Yi Chen, Qunfeng Liu, Yun Li “Benchmarks for Evaluating Optimization Algorithms and Benchmarking MATLAB Derivative-Free Optimizers for Practitioners ’ Rapid Access,†IEEE Access, Vol. 7,No. PP. 79657–79670, 2019.
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Published
2021-05-29
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Section
Articles
How to Cite
[1]
Y. W, A. Fauzan, A. Yani, and M. A. Aziz, “Analisis Performance Central Prosessing Unit (CPU) Realtime Menggunakan Metode Benchmarking”, MATRIK, vol. 20, no. 2, pp. 237–248, May 2021, doi: 10.30812/matrik.v20i2.1142.
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