Using MLPerf v4.1 data, the paper shows that per-GPU efficiency declines as GPU count grows, and it identifies intermediate configurations that balance training speed and resource use.
A container-based workflow for distribu- ted training of deep learning algorithms in hpc clusters,
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Estudio de la eficiencia en la escalabilidad de GPUs para el entrenamiento de Inteligencia Artificial
Using MLPerf v4.1 data, the paper shows that per-GPU efficiency declines as GPU count grows, and it identifies intermediate configurations that balance training speed and resource use.