A Frequency-aware Software Cache for Large Recommendation System Embeddings

• URL: http://arxiv.org/abs/2208.05321v1
• Date: Mon, 8 Aug 2022 12:08:05 GMT
• Title: A Frequency-aware Software Cache for Large Recommendation System Embeddings
• Authors: Jiarui Fang and Geng Zhang and Jiatong Han and Shenggui Li and Zhengda Bian and Yongbin Li and Jin Liu and Yang You
• Abstract summary: Deep learning recommendation models (DLRMs) have been widely applied in Internet companies. We propose a GPU-based software cache approaches to dynamically manage the embedding table in the CPU and GPU memory space. Our proposed software cache is efficient in training entire DLRMs on GPU in a synchronized update manner.
• Score: 11.873521953539361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning recommendation models (DLRMs) have been widely applied in Internet companies. The embedding tables of DLRMs are too large to fit on GPU memory entirely. We propose a GPU-based software cache approaches to dynamically manage the embedding table in the CPU and GPU memory space by leveraging the id's frequency statistics of the target dataset. Our proposed software cache is efficient in training entire DLRMs on GPU in a synchronized update manner. It is also scaled to multiple GPUs in combination with the widely used hybrid parallel training approaches. Evaluating our prototype system shows that we can keep only 1.5% of the embedding parameters in the GPU to obtain a decent end-to-end training speed.

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