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SCRec: A Scalable Computational Storage System with Statistical Sharding and Tensor-train Decomposition for Recommendation Models

arXiv 2504.00520 ↗First posted 1 Apr 2025cs.AR cs.LGcited by 0

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Abstract (arXiv)

Deep Learning Recommendation Models (DLRMs) play a crucial role in delivering personalized content across web applications such as social networking and video streaming. However, with improvements in performance, the parameter size of DLRMs has grown to terabyte (TB) scales, accompanied by memory bandwidth demands exceeding TB/s levels. Furthermore, the workload intensity within the model varies based on the target mechanism, making it difficult to build an optimized recommendation system. In this paper, we propose SCRec, a scalable computational storage recommendation system that can handle TB-scale industrial DLRMs while guaranteeing high bandwidth requirements. SCRec utilizes a software framework that features a mixed-integer programming (MIP)-based cost model, efficiently fetching data based on data access patterns and adaptively configuring memory-centric and compute-centric cores. Additionally, SCRec integrates hardware acceleration cores to enhance DLRM computations, particularly allowing for the high-performance reconstruction of approximated embedding vectors from extremely compressed tensor-train (TT) format. By combining its software framework and hardware accelerators, while eliminating data communication overhead by being implemented on a single server, SCRec achieves substantial improvements in DLRM inference performance. It delivers up to 55.77× speedup compared to a CPU-DRAM system with no loss in accuracy and up to 13.35× energy efficiency gains over a multi-GPU system.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 53 references that are in the map. First posted on arXiv 1 Apr 2025.

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References · 23

  1. 2016Wide & Deep Learning for Recommender Systemscited by 745
  2. 2019Deep Learning Recommendation Model for Personalization and Recommendation Systemscited by 327
  3. 2019The Architectural Implications of Facebook's DNN-based Personalized Recommendationcited by 83
  4. 2021Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Modelscited by 78
  5. 2019Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systemscited by 68
  6. 2020Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systemscited by 59
  7. 2021TT-Rec: Tensor Train Compression for Deep Learning Recommendation Modelscited by 58
  8. 2020DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inferencecited by 54
  9. 2019RecNMP: Accelerating Personalized Recommendation with Near-Memory Processingcited by 49
  10. 2019TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learningcited by 42
  11. 2020Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systemscited by 37
  12. 2021RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inferencecited by 35
11 more
  1. 2022RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendationcited by 28
  2. 2019Tensorized Embedding Layers for Efficient Model Compressioncited by 27
  3. 2022Merlin HugeCTR: GPU-accelerated Recommender System Training and Inferencecited by 20
  4. 2022AutoShard: Automated Embedding Table Sharding for Recommender Systemscited by 17
  5. 2020Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelismcited by 15
  6. 2020Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Trainingcited by 13
  7. 2022Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwardscited by 11
  8. 2022BagPipe: Accelerating Deep Recommendation Model Trainingcited by 10
  9. 2021GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendationcited by 7
  10. 2024G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systemscited by 4
  11. 2017SimpleSSD: Modeling Solid State Drives for Holistic System Simulationcited by 2

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