Deep Learning Recommendation Model (DLRM)
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Heterogeneous Acceleration Pipeline for Recommendation System Training () []
UBC & GaTech
Hotline: a runtime framework.
Utilize CPU main memory for non-popular embeddings and GPUs’ HBM for popular embeddings.
Fragment a mini-batch into popular and non-popular micro-batches (μ-batches).
Accelerating Neural Recommendation Training with Embedding Scheduling () [] [] []
HKUST
Herald: an adaptive location-aware inputs allocator to determine where embeddings should be trained and an optimal communication plan generator to determine which embeddings should be synchronized.
Bagpipe: Accelerating Deep Recommendation Model Training () []
UW-Madison & UChicago
DisaggRec: Architecting Disaggregated Systems for Large-Scale Personalized Recommendation (arXiv 2212.00939) [] []
Meta AI & WashU & UPenn & Cornell & Intel
Disaggregated system; decouple CPUs and memory resources; partition embedding tables.
UMich SymbioticLab & Meta
In-training pruning.
SJTU
A unified multi-GPU cache system.
Used for GNN training and DLR inference.
UChicago & Beijing University of Technology & Bandung Institute of Technology, Indonesia & Seagate Technology & Emory
A caching layer optimized for embedding access patterns.
Tencent & Edinburgh
P2P model update dissemination.
DLRM: Deep Learning Recommendation Model
AdaEmbed: Adaptive Embedding for Large-Scale Recommendation Models () []
UGache: A Unified GPU Cache for Embedding-based Deep Learning () [] []
EVStore: Storage and Caching Capabilities for Scaling Embedding Tables in Deep Recommendation Systems () [] [] []
Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update () []