# Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs

arXiv: https://arxiv.org/abs/1603.03820 · HTML: https://mlsanity.com/paper/1603.03820/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2016.

## Built on by (most cited first, 4 of 4)

- [ALX: Large Scale Matrix Factorization on TPUs](https://mlsanity.com/paper/2112.02194/) (2021)
- [Efficient Mixed Dimension Embeddings for Matrix Factorization](https://mlsanity.com/paper/2205.11248/) (2022)
- [Safe Collaborative Filtering](https://mlsanity.com/paper/2306.05292/) (2023)
- [HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs](https://mlsanity.com/paper/2304.07334/) (2023)

## Abstract (arXiv)

Matrix factorization (MF) is employed by many popular algorithms, e.g., collaborative filtering. The emerging GPU technology, with massively multicore and high intra-chip memory bandwidth but limited memory capacity, presents an opportunity for accelerating MF much further when appropriately exploiting the GPU architectural characteristics. This paper presents cuMF, a CUDA-based matrix factorization library that implements memory-optimized alternate least square (ALS) method to solve very large-scale MF. CuMF uses a variety set of techniques to maximize the performance on either single or multiple GPUs. These techniques include smart access of sparse data leveraging GPU memory hierarchy, using data parallelism in conjunction with model parallelism, minimizing the communication overhead between computing units, and utilizing a novel topology-aware parallel reduction scheme. With only a single machine with four Nvidia GPU cards, cuMF can be 6-10 times as fast, and 33-100 times as cost-efficient, compared with the state-of-art distributed CPU solutions. Moreover, this cuMF can solve the largest matrix factorization problem ever reported yet in current literature, while maintaining impressively good performance.