Architectural Implications of Graph Neural Networks
Graph neural networks (GNN) represent an emerging line of deep learning models that operate on graph structures. It is becoming more and more popular due to its high accuracy achieved in many graph-related tasks. However, GNN is not as well understood in the system and architecture community as its counterparts such as multi-layer perceptrons and convolutional neural networks. This work tries to introduce the GNN to our community. In contrast to prior work that only presents characterizations of GCNs, our work covers a large portion of the varieties for GNN workloads based on a general GNN description framework. By constructing the models on top of two widely-used libraries, we characterize the GNN computation at inference stage concerning general-purpose and application-specific architectures and hope our work can foster more system and architecture research for GNNs.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Architectural Implications of Embedding Dimension during GCN on CPU and GPU
Graph Neural Networks (GNNs) are a class of neural networks designed to extract information from the graphical structure of data. Graph Convolutional Networks (GCNs) are a widely used type of GNN for transductive graph l…
CPUGPUGraph LearningArchitectural Implications of Function-as-a-Service Computing
Serverless computing is a rapidly growing cloud application model, popularized by Amazon’s Lambda platform. Serverless cloud services provide fine-grained provisioning of resources, which scale automatically with user de…
Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications
With more scientific fields relying on neural networks (NNs) to process data incoming at extreme throughputs and latencies, it is crucial to develop NNs with all their parameters stored on-chip. In many of these applicat…
Architectural Implications of GNN Aggregation Programming Abstractions
Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN computation intensifies, a variety of progra…
Characterizing the Behavior of Training Mamba-based State Space Models on GPUs
Mamba-based State Space Models (SSM) have emerged as a promising alternative to the ubiquitous transformers. Despite the expressive power of transformers, the quadratic complexity of computing attention is a major impedi…
Text Generation