AutoShard: Automated Embedding Table Sharding for Recommender Systems. (arXiv:2208.06399v1 [cs.LG])

Embedding learning is an important technique in deep recommendation models to
map categorical features to dense vectors. However, the embedding tables often
demand an extremely large number of parameters, which become the storage and
efficiency bottlenecks. Distributed training solutions have been adopted to
partition the embedding tables into multiple devices. However, the embedding
tables can easily lead to imbalances if not carefully partitioned. This is a
significant design challenge of distributed systems named embedding table
sharding, i.e., how we should …

arxiv embedding lg recommender systems sharding systems