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 learning problems which apply convolution to learn information from graphs. GCN is a challenging algorithm from an architecture perspective due to inherent sparsity, low data reuse, and massive memory capacity requirements. Traditional neural algorithms exploit the high compute capacity of GPUs to achieve high performance for both inference and training. The architectural decision to use a GPU for GCN inference is a question explored in this work. GCN on both CPU and GPU was characterized in order to better understand the implications of graph size, embedding dimension, and sampling on performance.
Code (0)
등록된 구현이 없습니다.
Tasks
CPUGPUGraph LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Multimodal Cultural Heritage Architectural Style Classification for Residential Buildings in the UAE Based on CLIP Embeddings and SVM
The analysis and classification of cultural heritage architectural styles remain challenging due to the complexity of visual images of buildings, which are highly relied on in traditional CNN-based classification approac…
Dimensionality ReductionImages in Discrete Choice Modeling: Addressing Data Isomorphism in Multi-Modality Inputs
This paper explores the intersection of Discrete Choice Modeling (DCM) and machine learning, focusing on the integration of image data into DCM's utility functions and its impact on model interpretability. We investigate…
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 GenerationArchitectural 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…
Understanding Generalization from Embedding Dimension and Distributional Convergence
Deep neural networks often generalize well despite heavy over-parameterization, challenging classical parameter-based analyses. We study generalization from a representation-centric perspective and analyze how the geomet…