Graph Neural Networks at Scale: DGL with ROCm on AMD Hardware#
This blog introduces the Deep Graph Library (DGL) and explores its significance on AMD hardware for enabling scalable, performant graph neural networks.
Graphs power some of today’s most advanced AI workloads, from recommendation systems and fraud detection to drug discovery and scientific modeling. With DGL now ported to the ROCm platform, developers can run these cutting-edge graph neural networks on high-performance, cost-effective AMD GPUs — without sacrificing either performance or flexibility.
For AMD, this marks a major step toward enabling a broader class of AI applications and supporting a growing ecosystem of graph-driven innovation.
Get Started: Running DGL on ROCm#
Get Started with the ROCm-DGL Repo - GitHub Repository
Find All 4 Containerized Builds on Our DockerHub Dockerhub
Install DGL — Follow the Official Guide - DGL installation documentation
ROCm-DGL Compatibility Guide — Platforms, Versions, and More DGL compatibility
Why Does Deep Graph Learning Need a Specialized Stack?#
Most popular deep learning frameworks today — like PyTorch and TensorFlow — are optimized for dense tensor computations. This design works beautifully for structured data types such as images, text, or audio, where inputs can be neatly packed into regular shapes and processed efficiently on GPUs using highly optimized linear algebra kernels.
But graphs don’t follow those rules.
Graph data is inherently sparse, irregular, and often dynamic. You’re dealing with nodes that each have a varying number of neighbors, edges that may carry attributes, and topologies that defy the predictable structure of a grid. This irregularity introduces three big challenges:
Memory Access: Unlike dense tensors, graph operations often involve random memory access patterns — which are hard to optimize for GPU parallelism.
Batched Computation: Batching across graphs (or subgraphs) isn’t straightforward. Each sample might have a different shape or have a different number of nodes and edges.
Semantic Gap: Deep learning frameworks are designed around layers and tensors. But graph algorithms think in terms of nodes, edges, and message passing. Trying to implement GNNs with just tensor ops leads to verbose, error-prone, and inefficient code.
In short, there’s a mismatch between what the compute model graphs require and what general-purpose DL frameworks provide out of the box.
Deep Graph Learning needs its own abstraction — one that natively understands and optimizes the structure and semantics of graph data. That’s where libraries like DGL come in.
What is DGL, and Why Was It Built?#
At its core, the Deep Graph Library (DGL) is a framework purpose-built to bring structure-aware learning to the deep learning ecosystem. While traditional DL frameworks have made incredible strides in computer vision, language, and audio — all of which rely on dense, grid-like data — they begin to break down when faced with the irregularity of graph-structured data.
DGL steps in to close that gap.
Instead of forcing graph computations into the rigid mold of tensor operations, DGL flips the paradigm: it makes the graph itself the primary abstraction. Nodes, edges, neighborhoods, and connectivity aren’t treated as secondary metadata — they are front and center in the programming model.
Under the hood, DGL translates these graph-centric computations into a series of sparse tensor operations. That means you still benefit from the raw performance of modern deep learning frameworks like PyTorch or TensorFlow — but with a semantic layer that understands how graphs work.
In short, DGL was designed to:
Bridge the gap between dense tensor-centric frameworks and the sparse, relational nature of graphs.
Abstract away the low-level complexity of graph computation while still giving developers full control when needed.
Enable portable, backend-agnostic development, so models can run on your framework of choice with minimal friction.
By treating graphs as first-class citizens and distilling GNN computation into a small set of reusable primitives, DGL brings structure, scalability, and simplicity to a domain that sorely needed all three.
Rethinking the Building Blocks: DGL’s Design Principles#
At the heart of DGL is a powerful insight: most Graph Neural Networks (GNNs) — no matter how complex — can be reduced to a small number of core operations. DGL abstracts these into customizable message-passing primitives, which represent the fundamental stages of computation in a GNN: message, aggregate, and update.
These aren’t just fancy terms. They’re the reason DGL is both powerful and efficient.
Message Passing as a First-Class Citizen#
Each node in a graph gathers information from its neighbors, processes it, and updates its own state. This process — the essence of GNNs — maps directly onto DGL’s design:
Send (Message): Nodes “send” messages to their neighbors using learnable functions.
Aggregate: Each node collects incoming messages from neighbors.
Update: Nodes update their own state based on the aggregated messages.
By reducing GNNs to this pattern, DGL can:
Generalize across different GNN architectures (GCNs, GATs, GraphSAGE, etc.).
Decouple model logic from execution logic, allowing for backend and hardware optimizations.
Streamline both forward and backward passes, meaning the same primitives can be used during inference and gradient computation.
This abstraction also enables low-level kernel optimizations under the hood — think better memory locality, kernel fusion, reduced data movement — without the user having to worry about any of it.
In other words, these primitives are not just functional; they are designed for performance and lay a strong foundation for scaling GNN workloads to large graphs and modern hardware.
Graph-First Thinking: Making the Graph the Main Abstraction#
If you’ve tried implementing GNNs manually in PyTorch or TensorFlow, you know it can feel like swimming upstream. You end up writing complex code to manage edges, neighborhoods, batch construction, and sparse updates — and before you know it, the elegance of your model is buried under layers of boilerplate.
DGL flips that paradigm completely.
Rather than asking developers to think in terms of tensors and indices, DGL makes the graph itself the core programming abstraction. You describe your graph, define how information flows along its edges, and DGL handles the rest — from memory-efficient batching to edgewise parallelism.
This change in mental model does more than just simplify code:
It lets researchers focus on modeling, not memory layouts.
It abstracts away tedious graph engineering — like sampling neighbors, normalizing adjacency matrices, or batching variable-sized graphs.
It unlocks new optimizations: by owning the graph structure, DGL can perform smart things like:
Caching frequently accessed neighborhoods,
Optimizing data layouts for GPU memory,
Dynamically partitioning graphs for distributed training.
In essence, DGL treats the graph as a dynamic, learnable, high-performance data structure — not just a container of edges. This is a significant shift and enables DGL to scale GNNs both horizontally (across GPUs or machines) and vertically (by optimizing compute and memory use on each device).
A Framework-Neutral Philosophy That Just Works#
One of DGL’s smartest design choices is that it doesn’t try to reinvent
the deep learning wheel. Instead of acting as a heavyweight, standalone
framework, DGL is designed to sit seamlessly on top of existing
ecosystems — including PyTorch, TensorFlow, and MXNet.
The figure below shows where DGL lies in the tech stack on top of existing ecosystems.
Figure 1. DGL overall architecture
This approach has a few big wins:
You can reuse familiar tools, models, and training loops without needing to relearn everything.
Porting models across frameworks becomes trivial, which is especially useful in fast-moving research or when production infrastructure is tied to a specific backend.
Most importantly, it avoids vendor lock-in—allowing you to experiment, prototype, and scale without being tied to a single stack.
By staying lightweight and interoperable, DGL future-proofs your work. Whether you’re building experimental GNN models in PyTorch or deploying production workloads in TensorFlow, DGL adapts to your environment — not the other way around.
Built for Scale: Parallelism and Pipeline Flexibility in DGL#
One of DGL’s biggest strengths lies not just in what it does — but how well it scales. Graphs in the real world are rarely small. Whether you’re working with a protein interaction network or a massive e-commerce graph, performance quickly becomes a bottleneck. DGL tackles this head-on with a thoughtful, multi-level parallelization strategy that extends from the smallest workloads to massive, distributed graph training.
Parallelism Where It Counts#
Graph computations are notoriously hard to parallelize efficiently due to their irregular memory access patterns. But DGL works around this using a blend of smart strategies:
Node-wise and edgewise batching: Enables parallelism across graph components while maintaining the connectivity structure that GNNs rely on.
Optimized scatter/gather kernels: These operations form the backbone of message passing and aggregation. DGL uses carefully tuned kernels to perform these efficiently across a wide range of workloads.
Memory-aware execution plans: Sparse data structures can quickly lead to inefficient memory use. DGL tracks and optimizes memory access patterns to reduce cache misses and unnecessary data movement. The result is smooth scaling from single-GPU setups to multi-GPU and fully distributed environments. You can go from prototyping on your laptop to training billion-edge graphs across clusters — without rewriting your model code.
A Modular Piece of a Bigger Pipeline#
While DGL shines in handling graph-specific computation, it doesn’t try to do everything. You won’t find built-in data preprocessing pipelines or feature engineering tools — and that’s by design.
Instead, DGL keeps its scope focused: core GNN training and inference.
This makes it highly modular and composable. You’re free to plug DGL into your existing machine learning pipeline — whether you’re doing data cleaning in Spark, feature extraction with NumPy or Pandas, or model deployment using ONNX. If your graph data is preprocessed elsewhere, DGL will step in at the perfect time to handle the graph computation efficiently.
This lean approach pays off especially in production, where you often have domain-specific preprocessing, feature pipelines, or custom data loaders. DGL doesn’t get in your way — it just handles the heavy lifting once your data is ready.
Optimizing DGL for AMD Platforms#
While DGL supports multiple ML frameworks by design, its GPU support relies on Nvidia’s proprietary CUDA C++, so it cannot run on other GPUs out of the box. This is where AMD steps in. Our HIP compatibility layer and translation tooling allowed us to easily port the CUDA code to run on AMD GPUs, achieving similar performance with minimal effort.
At AMD, we’ve contributed to enabling DGL to run efficiently on AMD GPUs using the ROCm software stack. These efforts include:
Supporting HIP-based kernel execution, allowing DGL to run on ROCm-compatible GPUs with minimal friction.
Providing prebuilt Docker containers that include DGL + ROCm, so researchers and practitioners can get started quickly.
Our implementation preserves full compatibility with NVIDIA GPUs and adheres to upstreaming guidelines.
You can find our fork and container setup here: AMD ROCm-optimized DGL GitHub repo, AMD Dockerhub for DGL containers
This enablement is a part of AMD’s broader commitment to building an open, performant AI ecosystem — one that makes machine learning, including deep graph learning, accessible across architectures.
Summary#
DGL’s design strikes a rare balance of research flexibility and production readiness. Its graph-centric abstractions simplify development, while its modular architecture ensures seamless integration into modern ML pipelines. With support for AMD’s powerful and cost-effective GPUs via the ROCm platform, DGL now enables scalable, high-performance graph neural network training across diverse computing environments.
This is the first post in a series exploring Deep Graph Library (DGL). In the upcoming blogs, we’ll dive deeper into real-world use cases — including a hands-on look at how DGL powers drug discovery through graph-based learning.
Acknowledgements#
This blog is inspired by the original work published by DGL. Full credit to the DGL authors-
Amazon Web Services AI Shanghai Lablet (ASAIL): Dongyu Ru, Hongzhi (Steve) Chen, Jian Zhang, Minjie Wang, Peiyuan Zhou, Quan Gan, Rui Ying, Tiajun Xiao, Tong He, Xiangkun Hu, Zhenkun Cai, Chao Ma, Jinjing Zhou, Mufei Li, Zihao Ye, Zheng Zhang
Amazon Web Services Machine Learning: Da Zheng, Xiang Song, Israt Nisa, George Karypis
NVIDIA: Chang Liu, Dominique LaSalle, Joe Eaton, Triston Cao, Xin Yao
New York University: Lingfan Yu, Yu Gai, Jinyang Li
Georgia Institute of Technology: Muhammed Fatih Balın, Ümit V. Çatalyürek
The authors would also like to acknowledge the broader AMD team whose contributions were instrumental in enabling DGL: Mukhil Azhagan Mallaiyan Sathiaseelan, Anuya Welling, Vicky Tsang, Tiffany Mintz, Pei Zhang, Debasis Mandal, Yao Liu, Phani Vaddadi, Vish Vadlamani, Ritesh Hiremath, Bhavesh Lad, Radha Srimanthula, Mohan Kumar Mithur, Phaneendr-kumar Lanka, Jayshree Soni, Amit Kumar, Leo Paoletti, Anisha Sankar, Ram Seenivasan, Aditya Bhattacharji, Marco Grond, Anshul Gupta, Saad Rahim
Additional Resources#
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