3 papers
cs.PF2026
Optimus: A Generic Operator-Level PyTorch Model Transformation Framework
Menglu Yu, Jiaqi Xu, Yuzhen Huang +19
In large-scale industrial applications, deep learning models that power recommendation and ranking have complex and diverse model architectures. These models are continuously devel…
cs.DC2025
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…
cs.LG2024
CubicML: Automated ML for Large ML Systems Co-design with ML Prediction of Performance
Wei Wen, Quanyu Zhu, Weiwei Chu +2
Scaling up deep learning models has been proven effective to improve intelligence of machine learning (ML) models, especially for industry recommendation models and large language…