Python-based social science applications’ profiling and optimization on HPC systems using task and data parallelism
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https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.3.48Keywords:
Python-based social science applications, High-performance computing systems, task and data parallelism, Optimization methodology, Machine learning model evaluationDimensions Badge
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This research addresses the pressing need to optimize Python-based social science applications for high-performance computing (HPC)Abstract
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.
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