ISSN: XXXX-XXXX
Abhi International Journal of
Scientific Computing ( AIJSC )
Login/Register
For Reference
About Journal Aim and Scope Editorial Board Publication Ethics Indexing And Abstracting Peer Review Process About Journal Aim and Scope Editorial Board Publication Ethics Indexing And Abstracting Peer Review Process

Advancements in Parallel Computing for High-Performance Scientific Simulations

Authors: Lalit Sharma

Published: 2025-01-07 11:23:26.258

Download PDF

Abstract

This research examines the improvements in parallel computing technologies, including multi-core processors and distributed computing systems, as well as their implications on scientific simulations. The work looks into how such technologies improve computational speed, efficiency, and accuracy while handling implementation challenges. Using qualitative approaches such as interviewing researchers and analyzing case studies, the research reveals insights into how modern parallel computing techniques are revolutionizing the scientific research agenda, such as climate modeling and molecular simulations. It underlines aspects of performance vs. energy consumption, difficulties in technology integration, and the opportunities for future breakthroughs in interdisciplinary applications. This paper gives an overview of the whole role of parallel computing within scientific simulations and the research progress, problems, and directions.

Keywords

Parallel Computing, Multi-Core Processors, Distributed Computing, Scientific Simulations, Computational Efficiency

References

  1. Bell, R., & Hennessy, J. (2009). Computer Architecture: A Quantitative Approach. Elsevier.
  2. Buyya, R., & Murshed, M. (2002). "GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing." Concurrency and Computation: Practice and Experience, 14(13), 1175-1220.
  3. Chen, H., & Xu, K. (2017). "Energy-Efficient Parallel Computing for Large-Scale Simulations." Journal of Computational Physics, 361, 472-481.
  4. Dalal, M. S., & Malik, M. R. (2015). "Optimized Algorithms for Multi-Core Processors in HighPerformance Simulations." International Journal of Computational Science, 29(4), 234-247.
  5. Gannon, D., & Thain, D. (2003). "Grid Computing: An Overview." Grid Computing: Making the Global Infrastructure a Reality, 4, 41-53.
  6. Gropp, W., & Lusk, E. (2004). Using MPI: Advanced Features of the Message-Passing Interface. MIT Press.
  7. Han, Y., & Kim, S. (2019). "Reducing Latency in Distributed Systems for High-Performance Simulations." Journal of Parallel and Distributed Computing, 135, 123-131.
  8. Herlihy, M., & Shavit, N. (2012). The Art of Multiprocessor Programming. Elsevier.
  9. Horowitz, S., & Litz, A. (2015). "Parallel Algorithms for Scientific Computing." Journal of Computational Science, 22(1), 122-133.
  10. Narendra Kumar, B. Srinivas and Alok Kumar Aggrawal: “Web Application Vulnerability Assessment” International Journal of Enterprise computing and Business Systems”, vol-1, 2011(https://www.atlantis-press.com/proceedings/cac2s-13/6377)
  11. Megha Singla, Mohit Dua and Narendra Kumar: “CNS using restricted space algorithms for finding a shortest path”. International Journal of Engineering Trends and Technology, 2(1), 48-54, 2011.( https://ijettjournal.org/archive/ijett-v2i1p204)
  12. Huang, L., & Carter, B. (2020). "Optimization of Distributed Computing for Real-Time Applications." Journal of Computational Science Advances, 15(3), 180-195.
  13. Johnson, R., & Kumar, P. (2018). "Scalable Machine Learning Algorithms for Big Data Applications." International Journal of Computational Intelligence, 34(2), 145-160.
  14. Anuj Kumar, Narendra Kumar and Alok Aggrawal: “Balancing Exploration and Exploitation using Search Mining Techniques” in IJETT, 3(2), 158-160, 2012
  15. Anuj Kumar, Shilpi Srivastav, Narendra Kumar and Alok Agarwal “Dynamic Frequency Hopping: A Major Boon towards Performance Improvisation of a GSM Mobile Network” International Journal of Computer Trends and Technology, vol 3(5) pp 677-684, 2012.
  16. Kumar, P., & Ramasamy, M. (2011). "Power-Efficient Multi-Core Computing in Scientific Research." International Journal of High-Performance Computing Applications, 25(2), 113-122.
  17. Smith, T., & Lee, Y. (2016). "Parallel Computing Systems: Efficiency, Scalability, and Security." Journal of Distributed Computing, 28(3), 210-221.
  18. Trefethen, L. N., & Weideman, J. A. (2014). "Computational Techniques in Scientific Simulations." Numerical Linear Algebra with Applications, 21(4), 561-575.
  19. Williams, S., & Waterman, A. (2015). "Optimizing Performance in High-Performance Parallel Systems." International Journal of Parallel Programming, 43(5), 476-490.
  20. Zhang, Y., & Park, K. (2021) "Artificial Intelligence in Distributed Computing: A Review of Methods and Applications." Journal of Intelligent Computing and Applications, 14(3), 234-249. 890-903.
Download PDF

How to Cite

Lalit Sharma, (2025-01-07 11:23:26.258). Advancements in Parallel Computing for High-Performance Scientific Simulations. Abhi International Journal of Scientific Computing, Volume vZH6gXCrmWsFuEzJN3hb, Issue 1.

Follow Us
Get In Touch

Vitthal Namdev Nagar, Opp Jain Temple Sanganer Sanganer Bazar Jaipur 302029 Rajasthan (India)

mail@abhijournals.com

© Abhi International Journals. All Rights Reserved.