240 citations · 322 across the 4 of their papers we have counts for
4 papers
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…
TPP: Transparent Page Placement for CXL-Enabled Tiered-Memory
Hasan Al Maruf, Hao Wang, Abhishek Dhanotia +7
The increasing demand for memory in hyperscale applications has led to memory becoming a large portion of the overall datacenter spend. The emergence of coherent interfaces like CX…
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…
Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training
Mark Zhao, Niket Agarwal, Aarti Basant +14
Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators (DSA) are used to train increasingly-complex deep learning models. These clusters rely…