The Significance and Applications of Parallel Computing in the Modern Era

Year : 2025 | Volume : 12 | Issue : 01 | Page : 24 38
    By

    V. Basil Hans,

  1. Research Professor, Department of Management & Commerce, Srinivas University, Mangaluru, Karnataka, India

Abstract

This article explores the advancements in parallel computing, focusing on its applications in various domains such as scientific simulations, big data analytics, artificial intelligence, and real-time processing. We discuss the architectural shifts from traditional single-core processors to multi-core and many-core systems, along with the role of graphics processing unit (GPU)-based computing and specialized hardware like tensor processing units (TPUs) and field programmable gate arrays (FPGAs). Furthermore, the article examines contemporary software frameworks and programming models such as OpenMP, message passing interface (MPI), and compute unified device architecture (CUDA), which have enabled developers to harness the full potential of parallelism. With the explosion of data-driven applications, parallel computing plays a critical role in reducing computational time, enhancing efficiency, and enabling more complex problem-solving across industries. The article concludes with a discussion on the challenges faced in scalability, power consumption, and algorithm design, while also highlighting future trends in quantum computing and exascale systems.

Keywords: Artificial intelligence, quantum computing, vast datasets, computing architectures, computing

[This article belongs to Recent Trends in Parallel Computing ]

How to cite this article:
V. Basil Hans. The Significance and Applications of Parallel Computing in the Modern Era. Recent Trends in Parallel Computing. 2025; 12(01):24-38.
How to cite this URL:
V. Basil Hans. The Significance and Applications of Parallel Computing in the Modern Era. Recent Trends in Parallel Computing. 2025; 12(01):24-38. Available from: https://journals.stmjournals.com/rtpc/article=2025/view=193069


References

  1. Qadir Z, Le KN, Saeed N, Munawar HS. Towards 6G internet of things: recent advances, use cases, and open challenges. ICT Express. 2023; 9 (3): 296–312.
  2. Asanovic K, Bodik R, Demmel J, Keaveny T, Keutzer K, Kubiatowicz J, Morgan N, Patterson D, Sen K, Wawrzynek J, Wessel D. A view of the parallel computing landscape. Commun ACM. 2009; 52 (10): 56–67.
  3. Cirne W, Berman F. A comprehensive model of the supercomputer workload. In: Proceedings of the Fourth Annual IEEE International Workshop on Workload Characterization. WWC-4 (Cat. No. 01EX538), Austin, TX, USA, December 2, 2001. pp. 140–148.
  4. Venkatachalam V, Franz M. Power reduction techniques for microprocessor systems. ACM Comput Surv. 2005; 37 (3): 195–237.
  5. Cockshott P, Michaelson G. Orthogonal parallel processing in vector Pascal. Computer Lang Syst Struct. 2006; 32 (1): 2–41.
  6. Buluç A, Madduri K. Parallel breadth-first search on distributed memory systems. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, Seattle, WA, USA, November 12–18, 2011. pp. 1–12.
  7. Castañeda A, Rajsbaum S, Raynal M. The renaming problem in shared memory systems: an introduction. Computer Sci Rev. 2011; 5 (3): 229–251.
  8. Fernando E, Murad DF, Wijanarko BD. Classification and advantages parallel computing in process computation: a systematic literature review. In: 2018 International Conference on Computing, Engineering, and Design (ICCED), Bangkok, Thailand, September 6–8, 2018. pp. 143–147.
  9. Bacon DF, Graham SL, Sharp OJ. Compiler transformations for high-performance computing. ACM Comput Surv. 1994; 26 (4): 345–420.
  10. Jaiswal A, Arun CJ. Potential of artificial intelligence for transformation of the education system in India. Int J Educ Dev Inform Commun Technol. 2021; 17 (1): 142–158.
Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 07/11/2024
Accepted 18/11/2024
Published 08/01/2025
403

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