Heterogeneous Acceleration Pipeline for Recommendation System Training

• URL: http://arxiv.org/abs/2204.05436v2
• Date: Sun, 28 Apr 2024 05:44:15 GMT
• Title: Heterogeneous Acceleration Pipeline for Recommendation System Training
• Authors: Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan, Prashant J. Nair,
• Abstract summary: Recommendation models rely on deep learning networks and large embedding tables. These models are typically trained using hybrid-GPU or GPU-only configurations. This paper introduces Hotline, a heterogeneous CPU acceleration pipeline.
• Score: 1.8457649813040096
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recommendation models rely on deep learning networks and large embedding tables, resulting in computationally and memory-intensive processes. These models are typically trained using hybrid CPU-GPU or GPU-only configurations. The hybrid mode combines the GPU's neural network acceleration with the CPUs' memory storage and supply for embedding tables but may incur significant CPU-to-GPU transfer time. In contrast, the GPU-only mode utilizes High Bandwidth Memory (HBM) across multiple GPUs for storing embedding tables. However, this approach is expensive and presents scaling concerns. This paper introduces Hotline, a heterogeneous acceleration pipeline that addresses these concerns. Hotline develops a data-aware and model-aware scheduling pipeline by leveraging the insight that only a few embedding entries are frequently accessed (popular). This approach utilizes CPU main memory for non-popular embeddings and GPUs' HBM for popular embeddings. To achieve this, Hotline accelerator fragments a mini-batch into popular and non-popular micro-batches. It gathers the necessary working parameters for non-popular micro-batches from the CPU, while GPUs execute popular micro-batches. The hardware accelerator dynamically coordinates the execution of popular embeddings on GPUs and non-popular embeddings from the CPU's main memory. Real-world datasets and models confirm Hotline's effectiveness, reducing average end-to-end training time by 2.2x compared to Intel-optimized CPU-GPU DLRM baseline.

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