Emerging Paradigms in Parallel Computing: Trends and Innovations

Year : 2025 | Volume : 12 | Issue : 01 | Page : 39 43
    By

    Manmeet Kaur Arora,

  • Sahil Lal,

  • Anjali Raghav,

  • Tarun Kaushik,

  • Bhupinder Singh,

  1. Research Scholar, Sharda School of Law, Sharda University, Greater Noida, Uttar Pradesh, India
  2. Research Scholar, Sharda School of Law, Sharda University, Greater Noida, Uttar Pradesh, India
  3. Research Scholar, School of Law, Sharda University, Greater Noida, Uttar Pradesh, India
  4. Assistant Professor, Sharda School of Law, Sharda University, Greater Noida, Uttar Pradesh, India
  5. Professor, Sharda School of Law, Sharda University, Greater Noida, Uttar Pradesh, India

Abstract

Parallel computing is at an inflection point with revolutionary new paradigms and technologies. The goal of this paper is to survey the recent trend in parallel computing from architecture, programming model and applications. Mahajan cites a litany of architectural developments such as heterogeneous computing systems with integrated graphics processing unit/central processing unit ; the emerging promise from quantum and neuromorphic architectures (please see later); advances in packing transistors using novel materials, advanced geometries enabled at node scaling margins by innovative patterning strategies. Data parallelism and functional parallelism) are budding programming paradigms that promise more efficient execution of a given task in an enhanced way. The paper also discusses the need for parallel programming environments to support exploratory and innovative learning. Additionally, these emerging paradigms are applied to myriad domains including high-performance computing, big data analytics and artificial intelligence in which parallelism is essential for processing large volumes of data along with complex algorithms. Given the growing need for faster and more efficient computing solutions, being able to anticipate these new paradigms is important both in assisting researchers as well as professionals. This white paper intends to guide the future of parallel computing and attempt to create a space for further research and move towards effectively utilizing these advances.

Keywords: Parallel computing, programming model, graphics processing unit (GPU), central processing unit (CPU), neuromorphic architectures

[This article belongs to Recent Trends in Parallel Computing ]

How to cite this article:
Manmeet Kaur Arora, Sahil Lal, Anjali Raghav, Tarun Kaushik, Bhupinder Singh. Emerging Paradigms in Parallel Computing: Trends and Innovations. Recent Trends in Parallel Computing. 2025; 12(01):39-43.
How to cite this URL:
Manmeet Kaur Arora, Sahil Lal, Anjali Raghav, Tarun Kaushik, Bhupinder Singh. Emerging Paradigms in Parallel Computing: Trends and Innovations. Recent Trends in Parallel Computing. 2025; 12(01):39-43. Available from: https://journals.stmjournals.com/rtpc/article=2025/view=193071


References

  1. Zhang X, Sheng Y, Liu Z. Using expertise as an intermediary: unleashing the power of blockchain technology to drive future sustainable management using hidden champions. Heliyon. 2024; 10 (1): e23807.
  2. Khan T, Civas M, Cetinkaya O, Abbasi NA, Akan OB. Nanosensor networks for smart health care. In: Han B, Tomer VK, Nguyen TA, Farmani A, Singh PK, editors. Nanosensors for Smart Cities. Amsterdam, Netherlands: Elsevier; 2020. pp. 387–403.
  3. Agoulmine N, Kim K, Kim S, Rim T, Lee JS, Meyyappan M. Enabling communication and cooperation in bio-nanosensor networks: toward innovative healthcare solutions. IEEE Wireless Commun. 2012; 19 (5): 42–51.
  4. Dorj UO, Lee M, Choi JY, Lee YK, Jeong G. The intelligent healthcare data management system using nanosensors. J Sensors. 2017; 2017 (1): 7483075.
  5. Singh B, Kaunert C. Computational thinking for innovative solutions and problem-solving techniques: transforming conventional education to futuristic interdisciplinary higher education. In: Fonkam MB, Vajjhala NR, editors. Revolutionizing Curricula Through Computational Thinking, Logic, and Problem Solving. Hershey, PA, USA: IGI Global; 2024. pp. 60–82.
  6. Krichen M, Abdalzaher MS. Performance enhancement of artificial intelligence: a survey. J Netw Computer Appl. 2024; 232: 104034.
  7. Wang YE, Wei GY, Brooks D. Benchmarking TPU, GPU, and CPU platforms for deep learning. arXiv preprint arXiv:1907.10701. July 24, 2019. Available at https://arxiv.org/abs/1907.10701
  8. Singh B, Jain V, Kaunert C, Dutta PK, Singh G. Privacy matters: espousing blockchain and artificial intelligence (AI) for consumer data protection on e-commerce platforms in ethical marketing. In: Saluja S, Nayyar V, Rojhe K, Sharma S, editors. Ethical Marketing Through Data Governance Standards and Effective Technology. Hershey, PA, USA: IGI Global; 2024. pp. 167–184.
  9. Shafik W, Matinkhah M, Sanda MN. Network resource management drives machine learning: a survey and future research direction. J Commun Technol Electron Computer Sci. 2020; 2020: 1–5.
  10. Hiran KK, Doshi R, Patel M, editors. Applications of Virtual and Augmented Reality for Health and Wellbeing. Hershey, PA, USA: IGI Global; 2024.
  11. Mittal S, Vetter JS. A survey of CPU-GPU heterogeneous computing techniques. ACM Comput Surv. 2015; 47 (4): 1–35.
  12. Far YK, Shoumali P, Jourabchi AS, Mirzaei S, Salehian R, Mohammadi M, Aghazadeh S, Ghanbarzadeh E, Mohammadi AT. Computing the Future: Exploring the Frontier of Intelligent Technologies. Nobel Sciences; 2024.
  13. Dobre C, Xhafa F. Parallel programming paradigms and frameworks in big data era. Int J Parallel Programm. 2014; 42 (5): 710–738.
  14. Gill SS, Tuli S, Xu M, Singh I, Singh KV, Lindsay D, Tuli S, Smirnova D, Singh M, Jain U, Pervaiz H. Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: evolution, vision, trends and open challenges. Internet of Things. 2019; 8: 100118.
  15. Sanyaolu TO, Adeleke AG, Azubuko CF, Osundare OS. Harnessing blockchain technology in banking to enhance financial inclusion, security, and transaction efficiency. Int J Scholar Res Sci Technol. 2024; 5 (1): 35–53.
  16. Gryshova I, Balian A, Antonik I, Miniailo V, Nehodenko V, Nyzhnychenko Y. Artificial intelligence in climate smart in agricultural: toward a sustainable farming future. Access Sci Business Innov Digital Econ. 2024; 5 (1): 125–140.
  17. Estabrooks CA, Straus SE, Flood CM, Keefe J, Armstrong P, Donner GJ, Boscart V, Ducharme F, Silvius JL, Wolfson MC. Restoring trust: COVID-19 and the future of long-term care in Canada. Facets. 2020; 5 (1): 651–691.
  18. Li H, Zhu L, Shen M, Gao F, Tao X, Liu S. Blockchain-based data preservation system for medical data. J Med Syst. 2018; 42: 1–3.
  19. Rani P. Nanosensors and their potential role in internet of medical things. In: Kaushik S, Soni V, Skotti E, editors. Nanosensors for Futuristic Smart and Intelligent Healthcare Systems. Boca Raton, FL, USA: CRC Press; 2022. pp. 293–317.
  20. Rubeis G. Ethical implications of blockchain technology in biomedical research. Ethik in der Medizin. 2024; 36: 493–506.
  21. Ahirwar R, Khan N. Smart wireless nanosensor systems for human healthcare. In: Kaushik S, Soni V, Skotti E, editors. Nanosensors for Futuristic Smart and Intelligent Healthcare Systems. Boca Raton, FL, USA: CRC Press; 2022. pp. 265–292.
Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 25/07/2024
Accepted 09/12/2024
Published 08/01/2025
520

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