Heterogeneous Acceleration Pipeline for Recommendation System Training
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866917651809304576 |
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| author | Adnan, Muhammad Maboud, Yassaman Ebrahimzadeh Mahajan, Divya Nair, Prashant J. |
| author_facet | Adnan, Muhammad Maboud, Yassaman Ebrahimzadeh Mahajan, Divya Nair, Prashant J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_05436 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Heterogeneous Acceleration Pipeline for Recommendation System Training Adnan, Muhammad Maboud, Yassaman Ebrahimzadeh Mahajan, Divya Nair, Prashant J. Hardware Architecture Artificial Intelligence Machine Learning 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. |
| title | Heterogeneous Acceleration Pipeline for Recommendation System Training |
| topic | Hardware Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2204.05436 |