91 citations · 505 across the 35 of their papers we have counts for
4 papers · 1 filter
Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training
Xin Zhang, Quanyu Zhu, Liangbei Xu +8
The increasing complexity of deep learning recommendation models (DLRM) has led to a growing need for large-scale distributed systems that can efficiently train vast amounts of dat…
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang +50
Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand…
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
Maxim Naumov, John Kim, Dheevatsa Mudigere +12
Large-scale training is important to ensure high performance and accuracy of machine-learning models. At Facebook we use many different models, including computer vision, video and…
Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments
Jiyan Yang, Xiangrui Meng, Michael W. Mahoney
In this era of large-scale data, distributed systems built on top of clusters of commodity hardware provide cheap and reliable storage and scalable processing of massive data. Here…