most citedRecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure

4 citations · 4 across the 1 of their papers we have counts for

collaborators

1 paper

cs.LG2022★ 4 cited

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…