The Effect Of The Number Of Processes On Multiple Processing Performance On Linux

Ardhian Ekawijana, Beri Noviansyah

Abstract


Processing large data sequentially is often inefficient and time-consuming. Parallel computing is a fundamental solution to accelerate computation by dividing tasks among multiple processing units. This research aims to analyze the influence of the number of processes on the performance of parallel computing implemented using the `fork()` system call on the Linux operating system. A C program was developed to perform a CPU-intensive task on a stock price dataset. Testing was conducted with varying numbers of processes: 1, 2, 4, and 8. The performance metrics measured were wall time, speedup, and efficiency. The test results show a significant reduction in execution time as the number of processes increases. The system achieved near-linear speedup (2.00x for 2 processes, 4.00x for 4 processes, and 7.93x for 8 processes) with high efficiency (>99%). These findings prove that the `fork()`-based multi-process approach is highly effective for CPU-bound tasks.

Keywords


Efficiency; Fork; Linux; Multi-Process; Parallel; Speedup.

Full Text:

PDF

References


A. Aziz, Z., Naseradeen Abdulqader, D., Sallow, A. B., & Khalid Omer, H. (2021b). Python Parallel Processing and Multiprocessing: A Rivew. Academic Journal of Nawroz University, 10(3), 345–354. https://doi.org/10.25007/ajnu.v10n3a1145

Amdahl, G. M. (1967). Validity of the single processor approach to achieving large scale computing capabilities. Spring Joint Computer Conference, 483–485.

Baumann, A., Appavoo, J., Krieger, O., & Roscoe, T. (2019). A fork() in the road. Proceedings of the Workshop on Hot Topics in Operating Systems, 14–22. https://doi.org/10.1145/3317550.3321435

Gene, D. R., & Amdahl, M. (1967). Validity of the single processor approach to achieving large scale computing capabilities.

Ha, M., & Kim, S. H. (2022). CCoW: Optimizing Copy-on-Write Considering the Spatial Locality in Workloads. Electronics (Switzerland), 11(3). https://doi.org/10.3390/electronics11030461

Kim, S. (2021). Efficient exact response time analysis for fixed priority scheduling in lowest priority first-based feasibility tests. IEEE Embedded Systems Letters, 13(3), 69–72. https://doi.org/10.1109/LES.2020.3025600

Mustopa, A., Nawawi, H. M., & Riyanto, V. (2024). Combination of Feature Extraction Methods for Identification of Diseases in Corn Leaves. IEEE.

Mustopa, A., Nawawi, H. M., Riyanto, V., Azis, M. A., Nawawi, I., & Wijaya, G. (2024). Combination of Feature Extraction Methods for Identification of Diseases in Corn Leaves. 2024 International Conference on Information Technology Research and Innovation (ICITRI), 192–197.

Oliveira, D., Chen, W., Pinto, S., & Mancuso, R. (2023). Investigating and Mitigating Contention on Low-End Multi-Core Microcontrollers. ACM International Conference Proceeding Series, 221–226. https://doi.org/10.1145/3576914.3587513

Prambadi, G. A. (2021, July 23). Lonjakan Data Digital Disebut Harus Diantisipasi. Republika. https://tekno.republika.co.id/berita/qwo7em456/lonjakan-data-digital-disebut-harus-diantisipasi

Rambo, E. A., Donyanavard, B., Seo, M., Maurer, F., Kadeed, T., De Melo, C. B., Maity, B., Surhonne, A., Herkersdorf, A., Kurdahi, F., Dutt, N., & Ernst, R. (2022). The Self-Aware Information Processing Factory Paradigm for Mixed-Critical Multiprocessing. IEEE Transactions on Emerging Topics in Computing, 10(1), 250–266. https://doi.org/10.1109/TETC.2020.3011663

Tanenbaum, A. S. (2015). MODERN OPERATING SYSTEMS FOURTH EDITION.

Velarde Martinez, A. (2019, May 21). Implement of a high-performance computing system for parallel processing of scientific applications and the teaching of multicore and parallel programming. https://doi.org/10.4995/inn2018.2018.8908

Zhang, R., & Ballard, D. H. (2020). Parallel neural multiprocessing with gamma frequency latencies. Neural Computation, 32(9), 1635–1663. https://doi.org/10.1162/neco_a_01301




DOI: https://doi.org/10.38038/vocatech.v8i1.288

Refbacks

  • There are currently no refbacks.


Vocatech : Vocational and Technology Journal
Unit Penelitian dan Pengabdian Masyarakat & Penjaminan Mutu
Akademi Komunitas Negeri Aceh Barat
Komplek STTU Alue Peunyareng, Ujong Tanoh Darat, Meureubo, Kabupaten Aceh Barat, Aceh 23615
Telp. (0655) 7110271
Email: vocatech@aknacehbarat.ac.id


Vocatech: Vocational Education and Technology Journal Published by:
Lembaga Penelitian dan Pengabdian Masyarakat & Penjaminan Mutu
Akademi Komunitas Negeri Aceh Barat


Indexed by:

GS2 logoCrossref logoGaruda logosinta-5 logosinta-5 logo

Creative Commons License logo

Vocatech: Vocational Education and Technology Journal Creative Commons Attribution-ShareAlike 4.0 International License.

Published by: Lembaga Penelitian dan Pengabdian Masyarakat & Penjaminan Mutu Akademi Komunitas Negeri Aceh Barat