222 citations · 397 across the 21 of their papers we have counts for
4 papers · 1 filter
FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models
Geet Sethi, Pallab Bhattacharya, Dhruv Choudhary +2
Sequence-based deep learning recommendation models (DLRMs) are an emerging class of DLRMs showing great improvements over their prior sum-pooling based counterparts at capturing us…
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
Mark Zhao, Dhruv Choudhary, Devashish Tyagi +9
We present RecD (Recommendation Deduplication), a suite of end-to-end infrastructure optimizations across the Deep Learning Recommendation Model (DLRM) training pipeline. RecD addr…
RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation
Geet Sethi, Bilge Acun, Niket Agarwal +3
We propose RecShard, a fine-grained embedding table (EMB) partitioning and placement technique for deep learning recommendation models (DLRMs). RecShard is designed based on two ke…
Learning Memory Access Patterns
Milad Hashemi, Kevin Swersky, Jamie A. Smith +5
The explosion in workload complexity and the recent slow-down in Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use recen…