Posts by James E. T. Smith
DGL in Depth: SE(3)-Transformer on ROCm 7
- 05 December 2025
In this post, we demonstrate how to run the SE(3)-Transformer efficiently with Deep Graph Library (DGL) on AMD ROCm, enabling high-performance 3D graph learning for complex geometric models. This builds on our previous blog, which highlighted DGL’s versatility across diverse graph neural network (GNN) workloads, validating functionality, compatibility, and usability.
DGL in the Real World: Running GNNs on Real Use Cases
- 20 August 2025
In our previous blog post, we introduced the Deep Graph Library (DGL) and highlighted how its support on the AMD ROCm platform unlocks scalable, performant graph neural networks (GNNs) on AMD GPUs. That post focused on the why — the growing relevance of graph workloads and what it means to bring that capability to AMD’s accelerated computing ecosystem.