Deep learning recommendation models (DLRMs) have gained widespread application across search, advertising, and e-commerce. Still, DLRMs present notable challenges as they depend heavily on large embedding tables to represent sparse features in recommendation systems. This raises concerns about both memory capacity and cost. Solid-state drives (SSDs) offer a cost-effective solution with a significantly larger capacity, but they introduce read amplification issues because of the mismatch between embedding size and SSD read granularity. Prior SSD embedding storage systems aim to tackle these challenges by employing hypergraph partitioning to co-locate co-appearing embeddings onto the same SSD page, alleviating read amplification. However, this approach has a drawback as it divides embeddings into completely disjoint clusters, limiting potential combinations between embeddings. In response to this limitation, we introduce MaxEmbed. Capitalizing on the extensive storage capacity of SSDs, Max-Embed effectively mines relationships between storage combinations of embeddings with replication, thereby enhancing the effective bandwidth of SSDs. Additionally, MaxEmbed incorporates a corresponding online service module for embedding query request handling, leveraging two key optimizations to reduce the overhead brought by replication. Our evaluations demonstrate that MaxEmbed boosts SSD embedding serving throughput by up to 18.7% under various settings.

@inproceedings{FanXJL24,
  title = {MaxEmbed: Maximizing SSD bandwidth utilization for huge embedding models serving},
  author = {Ruwen Fan and Minhui Xie and Haodi Jiang and Youyou Lu},
  booktitle = {ASPLOS},
  year = {2024},
  doi = {10.1145/3622781.3674172}
}