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Performance Optimization Techniques in Parallel Computing

PUBLISHED May 15, 2024 (DOI: https://doi.org/10.54985/peeref.2405p3097724)

NOT PEER REVIEWED

Authors

Dr. Fatima Inamdar1 , Aishwarya Shukla1 , Gandharva Thite1 , Atharva Doifode1 , Sakshi Aherkar1
  1. VIIT

Conference / event

BTech Project Presentation, VIIT, April 2024 (Pune, India)

Poster summary

Parallel computing, a modern cornerstone, transforms how we handle complex problems across domains, allowing tasks to run concurrently on multiple processors. This boosts speed, efficiency, and scalability. It's essential for tasks unmanageable with sequential methods, from scientific simulations to data analytics, driven by escalating performance needs due to larger datasets and intricate tasks. Performance hinges on optimization techniques like load balancing, while scalability is crucial for handling big data and cloud computing demands. Our paper delves into optimizing performance and scalability, covering strategies from load balancing to communication optimization. Understanding and tackling these challenges unlock parallel computing's full potential, propelling computational science and technology forward.

Keywords

Parallel Computing, Load Balancing, Optimization, GPU Acceleration, Parallel Algorithm, Profiling Tools

Research areas

Education, Systems Science, Computer and Information Science

References

  1. S. Williams, A. Waterman, and D. Patterson, "Roofline: An insightful visual performance model for multicore architectures," Communica-tions of the ACM, vol. 52, no. 4, pp. 65-76, 2009.
  2. D. A. Bader and K. Madduri, "Designing parallel algorithms," CRC Press, 2010.
  3. J. Dongarra, I. Foster, G. Fox, W. Gropp, K. Kennedy, L. Torczon, and A. White, "The Sourcebook of Parallel Computing," Morgan Kaufmann, 2002.

Funding

No data provided

Supplemental files

No data provided

Additional information

Competing interests
No competing interests were disclosed.
Data availability statement
The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.
Creative Commons license
Copyright © 2024 Inamdar et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Cite
Inamdar, D., Shukla, A., Thite, G., Doifode, A., Aherkar, S. Performance Optimization Techniques in Parallel Computing [not peer reviewed]. Peeref 2024 (poster).
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