![]() Multi-terabyte embedding tables, NeuroShard achieves 11.6% improvement inĮmbedding costs over the state-of-the-art, which translates to 6. When deployed in an ultra-large production DLRM with Outperforms the state-of-the-art on the benchmark sharding dataset, achieving Experiments show that NeuroShard significantly and consistently ShardingSphere-Proxy has the ability to create a logical database, connect data sources, create sharding rules, automatically create sub-tables on the underlying database when creating logical tables, and perform query distribution and aggregation. Table-wise sharding plans with beam search and greedy grid search, Then it identifies the best column-wise and NeuroShard pre-trains neural cost models on augmented tables to cover Database sharding is the process of dividing the data into partitions which can then be stored in multiple database instances. Recommendation models (DLRMs) and propose NeuroShard for embedding table We instantiate this idea in deep learning Auto sharding or data sharding is needed when a dataset is too big to be stored in a single database. A shard is an individual partition that exists on separate database server instance to spread load. Then perform an online search to identify the best sharding plans given any Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. Built upon this pre-trained cost model, we Neural network to predict the costs of all the possible shards, which serves asĪn efficient sharding simulator. ![]() The idea is to pre-train a universal and once-for-all In this work, we explore a "pre-train, and search" paradigm forĮfficient sharding. Partitioning is NP-hard, and estimating the costs accurately and efficiently isĭifficult. This capability is known as data-dependent routing. The costs is important in distributed training. The shard map also serves as the broker of database connections for requests that carry a sharding key. Download a PDF of the paper titled Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models, by Daochen Zha and 10 other authors Download PDF Abstract: Sharding a large machine learning model across multiple devices to balance
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