21 citations · 47 across the 9 of their papers we have counts for
9 papers
DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines
Chenyu Jiang, Zhen Jia, Shuai Zheng +2
Multi-task model training has been adopted to enable a single deep neural network model (often a large language model) to handle multiple tasks (e.g., question answering and text s…
CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs
Hanpeng Hu, Junwei Su, Juntao Zhao +4
Deep Neural Networks (DNNs) have shown excellent performance in a wide range of machine learning applications. Knowing the latency of running a DNN model or tensor program on a spe…
Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training
Borui Wan, Juntao Zhao, Chuan Wu
Distributed full-graph training of Graph Neural Networks (GNNs) over large graphs is bandwidth-demanding and time-consuming. Frequent exchanges of node features, embeddings and emb…
A Cognitive Stimulation Dialogue System with Multi-source Knowledge Fusion for Elders with Cognitive Impairment
Jiyue Jiang, Sheng Wang, Qintong Li +2
When communicating with elders with cognitive impairment, cognitive stimulation (CS) help to maintain the cognitive health of elders. Data sparsity is the main challenge in buildin…
Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform
Shiwei Zhang, Lansong Diao, Siyu Wang +7
We present Rhino, a system for accelerating tensor programs with automatic parallelization on AI platform for real production environment. It transforms a tensor program written fo…
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…