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The use of GPUs has proliferated for machine learning workflows and is nowconsidered mainstream for many deep learning models. Meanwhile, when trainingstate-of-the-art personal recommendation models, which consume the highestnumber of compute cycles at our large-scale datacenters, the use of GPUs camewith various challenges due to having both compute-intensive andmemory-intensive components. GPU performance and efficiency of theserecommendation models are largely affected by model architecture configurationssuch as dense and sparse features, MLP dimensions. Furthermore, these modelsoften contain large embedding tables that do not fit into limited GPU memory.The goal of this paper is to explain the intricacies of using GPUs for trainingrecommendation models, factors affecting hardware efficiency at scale, andlearnings from a new scale-up GPU server design, Zion.

Understanding Training Efficiency of Deep Learning Recommendation Models at Scale