Exploring the environmental effect of multi-GPU scaling on the energy-time-CO2e relationship during the training of vision deep learning models
DOI:
https://doi.org/10.24054/rcta.v2i48.4569Keywords:
energy-aware computing, green computing, energy-efficient deep learning, carbon footprintAbstract
GPU scaling is a widely used strategy to reduce the training time of deep vision models. However, a reduction in time does not necessarily imply a reduction in energy consumption. This work investigates the impact of GPU scaling on the energy-time-CO2 relationship during the training of a deep vision model (dataset: CIFAR-10, architecture: ResNet-50) under a fixed compute budget to minimize variability. Energy was measured directly at the component level by combining Intel RAPL counters for the CPU and accumulated GPU energy via NVIDIA NVML, and CO2e was estimated with CodeCarbon using a carbon intensity of 0.20 kgCO2e/kWh (sensitivity: 0.10-0.30). Twelve configurations were evaluated by combining scaling (from 1 to 2 GPUs), precision (FP32 vs. AMP), and per-GPU power limits (300/250/200 W). The results show that multi-GPU scaling does not always reduce total energy, and that system-level adjustments such as AMP and power capping can more consistently improve energy efficiency, identifying an operating region near 250 W per GPU that reduces energy by 4.6%-9.6% with a time penalty of 0.7%-1.9%. In addition, CodeCarbon estimates reproduce the trends but exhibit a median relative error of 4.09% compared to direct measurements. These findings suggest practical criteria for selecting training configurations that balance acceleration and energy sustainability in deep vision.
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Copyright (c) 2026 Carlos Nelson Henríquez Miranda, Malak Andrés Sánchez Cataño, German Sánchez Torres
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