40 citations · 40 across the 1 of their papers we have counts for
5 papers · 1 filter
A Unified CPU-GPU Protocol for GNN Training
Yi-Chien Lin, Gangda Deng, Viktor Prasanna
Training a Graph Neural Network (GNN) model on large-scale graphs involves a high volume of data communication and computations. While state-of-the-art CPUs and GPUs feature high c…
ARGO: An Auto-Tuning Runtime System for Scalable GNN Training on Multi-Core Processor
Yi-Chien Lin, Yuyang Chen, Sameh Gobriel +3
As Graph Neural Networks (GNNs) become popular, libraries like PyTorch-Geometric (PyG) and Deep Graph Library (DGL) are proposed; these libraries have emerged as the de facto stand…
HitGNN: High-throughput GNN Training Framework on CPU+Multi-FPGA Heterogeneous Platform
Yi-Chien Lin, Bingyi Zhang, Viktor Prasanna
As the size of real-world graphs increases, training Graph Neural Networks (GNNs) has become time-consuming and requires acceleration. While previous works have demonstrated the po…
HyScale-GNN: A Scalable Hybrid GNN Training System on Single-Node Heterogeneous Architecture
Yi-Chien Lin, Viktor Prasanna
Graph Neural Networks (GNNs) have shown success in many real-world applications that involve graph-structured data. Most of the existing single-node GNN training systems are capabl…
HP-GNN: Generating High Throughput GNN Training Implementation on CPU-FPGA Heterogeneous Platform
Yi-Chien Lin, Bingyi Zhang, Viktor Prasanna
Graph Neural Networks (GNNs) have shown great success in many applications such as recommendation systems, molecular property prediction, traffic prediction, etc. Recently, CPU-FPG…