Technical Summary: Energy Consumption of Dataframe Libraries for End-to-End Deep Learning Pipelines

Problem Statement

Efficient data handling is a critical bottleneck in the development and deployment of deep learning models, particularly regarding energy consumption. While existing literature benchmarks dataframe libraries (Pandas, Polars, Dask) in isolation or against simple analytics workloads, there is a significant gap in understanding their performance and energy efficiency when embedded within full end-to-end deep learning pipelines. Specifically, prior studies fail to capture the tight interaction between data loading, preprocessing, batch feeding, and GPU workloads during both training and inference. This paper addresses the lack of comprehensive energy profiling for these libraries in realistic deep learning contexts, where data handling directly impacts CPU/GPU utilization and overall sustainability.

Methodology

The authors conducted a comparative analysis integrating Pandas, Polars, and Dask into representative deep learning and machine learning pipelines.

• Experimental Setup: Experiments were performed on a workstation featuring a 16-core/32-thread CPU (2.10 GHz), 128 GB RAM, and an NVIDIA Tesla V100 GPU (32 GB HBM2). The software stack included Python 3.8.12, TensorFlow 2.8.0, PyTorch 1.12.1, and specific versions of Pandas (1.4.2), Polars (0.10.18), and Dask (2022.2.0).
• Datasets: Three representative datasets were used:
  • Insurance: A small tabular dataset (1,000 records) for regression/classification.
  • ML-1M (MovieLens): A medium-scale dataset (~1 million rows) for collaborative filtering and Neural Collaborative Filtering (NCF).
  • COCO: A large-scale image dataset (~118,000 images) for ResNet-50 (classification) and Mask R-CNN (object detection).
• Models: The study evaluated classical models (Random Forest, SVR, XGBoost) and deep learning architectures (TabNet, ResNet, Mask R-CNN, NCF).
• Measurement Protocol:
  • Each configuration was executed five times to calculate trimmed means and 95% confidence intervals, suppressing outliers.
  • CPU Metrics: Captured via Linux perf (runtime, CPU energy, memory).
  • GPU Metrics: Captured via pynvml (GPU energy, VRAM usage).
  • Energy Profiling: The framework tracked energy consumption at every stage: data loading, preprocessing, training, and inference.
• Implementation: A unified data processing pipeline was implemented for each library, handling data loading, preprocessing (imputation, encoding, normalization), and batching. Polars utilized lazy evaluation and zero-copy slicing; Dask used partition-based parallel processing; Pandas used standard indexing.

Key Contributions

The paper makes three primary contributions to the field of sustainable machine learning:

1. End-to-End Pipeline Integration: Unlike prior work focusing on isolated steps (e.g., data loading only), this study evaluates the impact of dataframe backends across the entire training and inference lifecycle, from data ingestion to model prediction.
2. Differentiated Training vs. Inference Analysis: The research explicitly compares how these libraries perform during training versus inference, addressing a gap in existing literature that often treats these phases as homogeneous.
3. Granular Energy Profiling: The study moves beyond runtime analysis to provide a detailed breakdown of energy consumption for both CPU and GPU, including specific measurements for RAM and VRAM usage. This allows for a complete energy profile of each dataloader setup.

Results

The experimental results reveal distinct performance and energy trade-offs based on dataset size and model complexity:

• Small Datasets (Insurance):
  • Runtime: Pandas and Polars exhibited nearly identical runtimes for simple models (Random Forest, SVR). Dask incurred higher runtime due to task scheduling overhead.
  • Energy: Polars showed lower CPU energy consumption for neural architectures (TabNet) compared to Pandas and Dask, attributed to its parallelized execution. However, Polars had a higher CPU memory footprint due to Arrow buffers.
• Medium-Scale Datasets (ML-1M):
  • Runtime: Polars significantly outperformed both Pandas and Dask for Collaborative Filtering and NCF workflows (e.g., 2.5s vs. 17.9s for Pandas in NCF). This was driven by efficient columnar operations and zero-copy transfers to PyTorch tensors.
  • Energy: Dask's partition scheduling overhead led to significantly higher CPU energy consumption compared to Polars and Pandas.
• Large-Scale Datasets (COCO):
  • Runtime: For heavy GPU workloads (ResNet, Mask R-CNN), all three libraries achieved similar runtimes, as the GPU computation dominated the pipeline.
  • Memory & Energy: Dask maintained higher CPU memory footprints due to partitioned data structures. Polars and Pandas maintained lower CPU memory footprints. Polars demonstrated marginal CPU energy savings during preprocessing, while GPU energy consumption remained virtually identical across backends as model computation dominated.
• Operator-Level Analysis: Table 2 indicates that Polars consistently exhibited lower CPU energy consumption for bulk operations across all models when dataset sizes exceeded a few hundred thousand rows.

Significance and Claims

The paper claims to bridge the gap between performance benchmarking and sustainable machine learning by providing the first comprehensive, end-to-end energy analysis of dataframe libraries in deep learning pipelines.

• Practical Recommendations:
  • Polars is recommended for energy-efficient processing of medium-to-large datasets, particularly for workflows involving complex data transformations or neural networks, due to its parallel execution and zero-copy capabilities.
  • Pandas remains competitive and viable for small-to-moderate workloads where ecosystem maturity is a priority.
  • Dask is suitable when data truly exceeds available RAM or distributed computing is required, though its overhead may lead to higher energy usage on smaller datasets.
• Sustainability Impact: The study quantifies that while GPU energy is often dominated by the model itself, the choice of dataloader significantly impacts CPU energy consumption and memory efficiency, offering actionable insights for reducing the carbon footprint of machine learning operations.

The authors conclude that while Polars shows promise for energy efficiency, the choice of library must be balanced against dataset size, model type, and hardware constraints. Future work is suggested to explore these libraries on multi-node clusters and emerging hardware accelerators.