Python-based social science applications’ profiling and optimization on HPC systems using task and data parallelism

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Published

26-09-2023

DOI:

https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.3.48

Keywords:

Python-based social science applications, High-performance computing systems, task and data parallelism, Optimization methodology, Machine learning model evaluation

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Issue

Vol. 14 No. 03 (2023): The Scientific Temper

Section

Research article

License

Copyright (c) 2023 The Scientific Temper

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Authors

  • S. Prabagar Department of Computer Science and Engineering, Alliance College of Engineering and Design, Alliance University, Bangalore, Karnataka, India
  • Vinay K. Nassa Department of Computer Science and Engineering, Rajarambapu Institute of Technology, Maharashtra, India.
  • Senthil V. M Department of computer application, Jyoti Nivas College, Bengaluru, Karnataka, India
  • Shilpa Abhang Department of computer application, Jyoti Nivas College, Bengaluru, Karnataka, India.
  • Pravin P. Adivarekar Department of Computer Engineering, A.P.Shah Institute of Technology, Thane, Maharashtra, India.
  • Sridevi R Department of Computer Science and Engineering, K. Ramakrishnan College of Engineering, Trichy, Tamil Nadu, India

Abstract

This research addresses the pressing need to optimize Python-based social science applications for high-performance computing (HPC)
systems, emphasizing the combined use of task and data parallelism techniques. The paper delves into a substantial body of research,
recognizing Python’s interpreted nature as a challenge for efficient social science data processing. The paper introduces a Python
program that exemplifies the proposed methodology. This program uses task parallelism with multi-processing and data parallelism
with dask to optimize data processing workflows. It showcases how researchers can effectively manage large datasets and intricate
computations on HPC systems. The research offers a comprehensive framework for optimizing Python-based social science applications
on HPC systems. It addresses the challenges of Python’s performance limitations, data-intensive processing, and memory efficiency.
Incorporating insights from a rich literature survey, it equips researchers with valuable tools and strategies for enhancing the efficiency
of their social science applications in HPC environments.

How to Cite

Prabagar, S., Nassa, V. K., M, S. V., Abhang, S., Adivarekar, P. P., & R, S. (2023). Python-based social science applications’ profiling and optimization on HPC systems using task and data parallelism. The Scientific Temper, 14(03), 870–876. https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.3.48

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