Energy Consumption of Dataframe Libraries for End-to-End Deep Learning Pipelines: A Comparative Analysis
A recent academic paper published on arXiv presents a comprehensive comparative analysis of three major Python data manipulation libraries: Pandas, Polars, and Dask. The study specifically examines their performance when integrated into complete deep learning training and inference pipelines. Addressing a significant gap in existing literature, the research investigates how these libraries interact with substantial GPU workloads during critical operational phases, including data loading, preprocessing, and batch feeding. The authors conducted rigorous measurements of key performance indicators such as runtime, memory usage, disk usage, and energy consumption for both CPU and GPU components. These metrics were evaluated across various machine learning models and datasets to provide a holistic view of efficiency. The findings aim to assist developers and researchers in selecting optimal data processing tools that balance computational speed with energy efficiency in high-performance computing environments. This work contributes to the growing field of sustainable artificial intelligence by highlighting the environmental impact of software library choices in deep learning workflows.
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Energy Consumption of Dataframe Libraries for End-to-End Deep Learning Pipelines: A Comparative Analysis
A recent academic paper published on arXiv presents a comprehensive comparative analysis of three major Python data manipulation libraries: Pandas, Polars, and Dask. The study specifically examines their performance when integrated into complete deep learning training and inference pipelines. Addressing a significant gap in existing literature, the research investigates how these libraries interact with substantial GPU workloads during critical operational phases, including data loading, preprocessing, and batch feeding. The authors conducted rigorous measurements of key performance indicators such as runtime, memory usage, disk usage, and energy consumption for both CPU and GPU components. These metrics were evaluated across various machine learning models and datasets to provide a holistic view of efficiency. The findings aim to assist developers and researchers in selecting optimal data processing tools that balance computational speed with energy efficiency in high-performance computing environments. This work contributes to the growing field of sustainable artificial intelligence by highlighting the environmental impact of software library choices in deep learning workflows.
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