5 citations · 8 across the 5 of their papers we have counts for
5 papers
Helios: An Efficient Out-of-core GNN Training System on Terabyte-scale Graphs with In-memory Performance
Jie Sun, Mo Sun, Zheng Zhang +6
Training graph neural networks (GNNs) on large-scale graph data holds immense promise for numerous real-world applications but remains a great challenge. Several disk-based GNN sys…
PyHGL: A Python-based Hardware Generation Language Framework
Jintao Sun, Zeke Wang, Tao Lu +1
Hardware generation languages (HGLs) increase hardware design productivity by creating parameterized modules and test benches. Unfortunately, existing tools are not widely adopted…
MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems
Guan Shen, Jieru Zhao, Zeke Wang +5
Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly. As a promising solution achieving high scalability and low manufacturing cost…
Legion: Automatically Pushing the Envelope of Multi-GPU System for Billion-Scale GNN Training
Jie Sun, Li Su, Zuocheng Shi +8
Graph neural network(GNN) has been widely applied in real-world applications, such as product recommendation in e-commerce platforms and risk control in financial management system…
P4SGD: Programmable Switch Enhanced Model-Parallel Training on Generalized Linear Models on Distributed FPGAs
Hongjing Huang, Yingtao Li, Jie Sun +5
Generalized linear models (GLMs) are a widely utilized family of machine learning models in real-world applications. As data size increases, it is essential to perform efficient di…