2023

  • EVStore: Storage and Caching Capabilities for Scaling Embedding Tables in Deep Recommendation Systems ASPLOS'23
  • GRACE: A Scalable Graph-Based Approach To Accelerating Recommendation Model Inference ASPLOS'23
  • AdaEmbed: Adaptive Embedding for Large-Scale Recommendation Models OSDI'23
  • FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models arxiv

2022

  • RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure arxiv
  • Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update OSDI'22
  • PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems arxiv
  • HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework VLDB'22
  • HET-GMP: a graph-based system approach to scaling large embedding model training SIGMOD'22
  • Fleche: an efficient GPU embedding cache for personalized recommendations EuroSys'22
  • RecShard: statistical feature-based memory optimization for industry-scale neural recommendation ASPLOS'22
  • BagPipe: Accelerating Deep Recommendation Model Training arxiv

2021

  • Accelerating recommendation system training by leveraging popular choices VLDB'21
  • SPACE: Locality-Aware Processing in Heterogeneous Memory for Personalized Recommendations ISCA'21
  • Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models arxiv ISCA'22
  • RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance MICRO'21

2020

  • Kraken: Memory-Efficient Continual Learning for Large-Scale Real-Time Recommendations SC'20
  • Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems MLSys'20
  • DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference ISCA'20