# Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training

arXiv: https://arxiv.org/abs/2010.13100 · HTML: https://mlsanity.com/paper/2010.13100/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Accelerating Recommendation System Training by Leveraging Popular Choices](https://mlsanity.com/paper/2103.00686/) (2021)
- [SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures](https://mlsanity.com/paper/2205.04711/) (2022)
- [Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards](https://mlsanity.com/paper/2205.04702/) (2022)
- [MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation](https://mlsanity.com/paper/2302.10872/) (2023)
- [RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance](https://mlsanity.com/paper/2105.08820/) (2021)
- [GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks](https://mlsanity.com/paper/2203.00158/) (2022)
- [DiVa: An Accelerator for Differentially Private Machine Learning](https://mlsanity.com/paper/2208.12392/) (2022)
- [PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models](https://mlsanity.com/paper/2406.14571/) (2024)
- [LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models](https://mlsanity.com/paper/2404.08847/) (2024)
- [Hera: A Heterogeneity-Aware Multi-Tenant Inference Server for Personalized Recommendations](https://mlsanity.com/paper/2302.11750/) (2023)
- [ElasticRec: A Microservice-based Model Serving Architecture Enabling Elastic Resource Scaling for Recommendation Models](https://mlsanity.com/paper/2406.06955/) (2024)
- [SCRec: A Scalable Computational Storage System with Statistical Sharding and Tensor-train Decomposition for Recommendation Models](https://mlsanity.com/paper/2504.00520/) (2025)

## Abstract (arXiv)

Personalized recommendations are one of the most widely deployed machine learning (ML) workload serviced from cloud datacenters. As such, architectural solutions for high-performance recommendation inference have recently been the target of several prior literatures. Unfortunately, little have been explored and understood regarding the training side of this emerging ML workload. In this paper, we first perform a detailed workload characterization study on training recommendations, root-causing sparse embedding layer training as one of the most significant performance bottlenecks. We then propose our algorithm-architecture co-design called Tensor Casting, which enables the development of a generic accelerator architecture for tensor gather-scatter that encompasses all the key primitives of training embedding layers. When prototyped on a real CPU-GPU system, Tensor Casting provides 1.9-21x improvements in training throughput compared to state-of-the-art approaches.