29 citations · 59 across the 4 of their papers we have counts for
4 papers · 1 filter
Optimizing DNN Compilation for Distributed Training with Joint OP and Tensor Fusion
Xiaodong Yi, Shiwei Zhang, Lansong Diao +6
This paper proposes DisCo, an automatic deep learning compilation module for data-parallel distributed training. Unlike most deep learning compilers that focus on training or infer…
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…
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads
Siyu Wang, Yi Rong, Shiqing Fan +6
The last decade has witnessed growth in the computational requirements for training deep neural networks. Current approaches (e.g., data/model parallelism, pipeline parallelism) pa…
DAPPLE: A Pipelined Data Parallel Approach for Training Large Models
Shiqing Fan, Yi Rong, Chen Meng +10
It is a challenging task to train large DNN models on sophisticated GPU platforms with diversified interconnect capabilities. Recently, pipelined training has been proposed as an e…