Harnessing the Power of Supercomputers: Solving Complex Mathematical Problems and Queueing Theory
DOI:
https://doi.org/10.36676/jrps.v15.i1.1398Keywords:
Supercomputers, Mathematical Problems, Computational Intensity, High-performance Computing, Parallel Processing, Distributed Computing, Real-world Applications, Queueing theoryAbstract
Supercomputers represent the pinnacle of computational power, offering unparalleled capabilities to tackle complex mathematical problems across various domains. This paper provides a comprehensive overview of the ways supercomputers are utilized in solving intricate mathematical issues, ranging from understanding the nature of these problems to exploring the challenges and future directions in the field.
Supercomputers play a critical role in the practical applications of queueing theory across various industries by enabling the analysis and optimization of complex systems that involve waiting lines and service processes. In telecommunications networks, for instance, supercomputers are used to model and optimize traffic flow, ensuring efficient data transmission and minimal latency. By simulating network conditions under different traffic loads and service protocols, supercomputers help identify optimal resource allocations and configurations, significantly improving network reliability and performance. This capability is crucial for managing the ever-increasing data demands of modern communication systems.
References
Acebrón, J. A., & Spigler, R. (2007). Supercomputing applications to the numerical modeling of industrial and applied mathematics problems. The Journal of Supercomputing, 40(1), 67–80. https://doi.org/10.1007/s11227-006-0014-3 DOI: https://doi.org/10.1007/s11227-006-0014-3
Bhargava, D., & Arora, D. (2008). Computer Researchers Harnessing DNA For Computing.
Kitchens, F. L., & Sharma, S. K. (2003). Affordable Supercomputing Solutions: Cluster Computers In Business Applications.
Liu, Y. Y., Cho, W. K. T., & Wang, S. (n.d.). PEAR: A Massively Parallel Evolutionary Computational Approach for Political Redistricting Optimization and Analysis.
Mullen, J., Byun, C., Gadepally, V., Samsi, S., Reuther, A., & Kepner, J. (2017). Learning by doing, High Performance Computing education in the MOOC era. Journal of Parallel and Distributed Computing, 105, 105–115. https://doi.org/10.1016/j.jpdc.2017.01.015 DOI: https://doi.org/10.1016/j.jpdc.2017.01.015
Ramouthar, R., & Seker, H. (2023). Hybrid Quantum-Classical Computing—A Fusion of Classical And Quantum Computational Substrates. https://doi.org/10.31730/osf.io/rj7cv DOI: https://doi.org/10.31730/osf.io/rj7cv
Sharma, M., Choudhary, V., Bhatia, R. S., Malik, S., Raina, A., & Khandelwal, H. (2021). Leveraging the power of quantum computing for breaking RSA encryption. Cyber-Physical Systems, 7(2), 73–92. https://doi.org/10.1080/23335777.2020.1811384 DOI: https://doi.org/10.1080/23335777.2020.1811384
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal for Research Publication and Seminar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Re-users must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. This license allows for redistribution, commercial and non-commercial, as long as the original work is properly credited.