34 citations · 102 across the 12 of their papers we have counts for
4 papers · 1 filter
Large-scale Machine Learning Cluster Scheduling via Multi-agent Graph Reinforcement Learning
Xiaoyang Zhao, Chuan Wu
Efficient scheduling of distributed deep learning (DL) jobs in large GPU clusters is crucial for resource efficiency and job performance. While server sharing among jobs improves r…
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
Tianfeng Liu, Yangrui Chen, Dan Li +7
Graph neural networks (GNNs) have extended the success of deep neural networks (DNNs) to non-Euclidean graph data, achieving ground-breaking performance on various tasks such as no…
Adversarial Deep Learning for Online Resource Allocation
Bingqian Du, Zhiyi Huang, Chuan Wu
Online algorithm is an important branch in algorithm design. Designing online algorithms with a bounded competitive ratio (in terms of worst-case performance) can be hard and usual…
OneFlow: Redesign the Distributed Deep Learning Framework from Scratch
Jinhui Yuan, Xinqi Li, Cheng Cheng +9
Deep learning frameworks such as TensorFlow and PyTorch provide a productive interface for expressing and training a deep neural network (DNN) model on a single device or using dat…