Parallel and Distributed Computation of a Fingerprint Access Control System


  • Bopatriciat Boluma Mangata University of Kinshasa, D.R.Congo
  • Kadima Muamba Faculty of Computer science, University of Reverend Kim, D.R.Congo
  • Fundji Khalaba Departement of Computer science, University of Kinshasa
  • Bukanga Christian Parfum Department of Computer science, University of Kinshasa, D.R. Congo
  • Kisiaka Mbambi Faculty of Computer science, University of Reverend Kim, D.R.Congo



task parallel library, fingerprint, biometrics, parallel computing


This work evaluates the runtime performance of a single-mode biometric recognition system for fingerprint-based access control to secure premises. To speed up the computation time in this system, we resorted to parallel programming, targeting more loops in the verification module. Our approach would therefore be to parallelize all loops that are computationally intensive during the verification of fingerprints in the database. On this, we exploited Microsoft's Task Parallel Library, specifically exploiting the for and for each loop. On the test set performed in sequential and parallel versions in the different data sizes, namely 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, and 600, we can state that the results obtained by the sequential and parallel implementations of our performance test programs allowed us to determine the best approach. Therefore, it is very clear that the sequential program is too greedy in terms of computation time compared to the parallel program which minimizes the computation time.


Download data is not yet available.


Abdellatif, M. (2016). Accéleration des traitements de la sécurité mobile avec le calcul parallèle (Doctoral dissertation, École de technologie supérieure).

Bopatriciat Boluma Mangata & al. (2022). Performance evaluation of a single access contol system. journal of research in engeneering and applied sciences. Volume (7 Issue 01), p4-6.

Bopatriciat Boluma Mangata et Al.(2021). Contribution of an Embedded and Biometric System in a Replicated Database for Access Control in a Multi-Entry Institution. International Journal of Science and Research (IJSR), Volume (10 Issue 3), p2-5.

Dall’Olio, D., Curti, N., Fonzi, E., Sala, C., Remondini, D., Castellani, G., & Giampieri, E. (2021). Impact of concurrency on the performance of a whole exome sequencing pipeline. BMC bioinformatics, 22(1), 1-15.

Fryza, T., Svobodova, J., Adamec, F., Marsalek, R., & Prokopec, J. (2012). Overview of parallel platforms for common high performance computing. Radioengineering, 21(1), 436-444.

Li, C., Peng, Y., Su, M., & Jiang, T. (2020). GPU Parallel Implementation for Real-Time Feature Extraction of Hyperspectral Images. Applied Sciences, 10(19), 6680.

Melnykov, V., Chen, W. C., & Maitra, R. (2012). MixSim: An R package for simulating data to study performance of clustering algorithms. Journal of Statistical Software, 51, 1-25.

Miao, Y., Tian, Y., Peng, L., Hossain, M. S., & Muhammad, G. (2017). Research and implementation of ECG-based biological recognition parallelization. IEEE Access, 6, 4759-4766.

Ocaña, K., & de Oliveira, D. (2015). Parallel computing in genomic research: advances and applications. Advances and applications in bioinformatics and chemistry: AABC, 8, 23.

Reumont-Locke, F. (2015). Méthodes efficaces de parallélisation de l'analyse de traces noyau (Doctoral dissertation, École Polytechnique de Montréal).

Rosenberg, D., Mininni, P. D., Reddy, R., & Pouquet, A. (2020). GPU parallelization of a hybrid pseudospectral geophysical turbulence framework using CUDA. Atmosphere, 11(2), 178.

Tavara, S., Schliep, A., & Basu, D. (2021, September). Federated Learning of Oligonucleotide Drug Molecule Thermodynamics with Differentially Private ADMM-Based SVM. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 459-467). Springer, Cham.

Wan, S., & Zou, Q. (2017). HAlign-II: efficient ultra-large multiple sequence alignment and phylogenetic tree reconstruction with distributed and parallel computing. Algorithms for Molecular Biology, 12(1), 1-10.

Williams-Young, D. B., De Jong, W. A., Van Dam, H. J., & Yang, C. (2020). On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters. Frontiers in chemistry, 951.




How to Cite

Boluma Mangata, B., Muamba, K., Khalaba, F., Christian Parfum, B., & Mbambi, K. . (2022). Parallel and Distributed Computation of a Fingerprint Access Control System. Journal of Computing Research and Innovation, 7(2), 1–10.



General Computing