19 citations · 33 across the 5 of their papers we have counts for
5 papers
Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning
Zhiyao Zhang, Myeung Suk Oh, FNU Hairi +3
Actor-critic methods for decentralized multi-agent reinforcement learning (MARL) facilitate collaborative optimal decision making without centralized coordination, thus enabling a…
Prediction-Assisted Online Distributed Deep Learning Workload Scheduling in GPU Clusters
Ziyue Luo, Jia Liu, Myungjin Lee +1
The recent explosive growth of deep learning (DL) models has necessitated a compelling need for efficient job scheduling for distributed deep learning training with mixed paralleli…
DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines
Ye Tian, Zhen Jia, Ziyue Luo +2
Diffusion models have emerged as dominant performers for image generation. To support training large diffusion models, this paper studies pipeline parallel training of diffusion mo…
Optimizing Task Placement and Online Scheduling for Distributed GNN Training Acceleration
Ziyue Luo, Yixin Bao, Chuan Wu
Training Graph Neural Networks (GNN) on large graphs is resource-intensive and time-consuming, mainly due to the large graph data that cannot be fit into the memory of a single mac…
Efficient Pipeline Planning for Expedited Distributed DNN Training
Ziyue Luo, Xiaodong Yi, Guoping Long +4
To train modern large DNN models, pipeline parallelism has recently emerged, which distributes the model across GPUs and enables different devices to process different microbatches…