2024/08/04 by Meng Wu, Ming Yan, Wu, Meng +8 · 2 citations
Engineering · Neuroscience · #Brain Tumor Detection and Classification #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #IoT-based Smart Home Systems #Robotics and Automated Systems
paper · pdf · doi:10.48550/arxiv.2408.01902
openalex publication_date 2024/08/04 · openalex created_date 2025/01/17 · openalex updated_date 2026/07/28
Characterizing and understanding graph neural networks (GNNs) is essential for identifying performance bottlenecks and facilitating their deployment in parallel and distributed systems. Despite substantial work in this area, a comprehensive survey on characterizing and understanding GNNs from a computer architecture perspective is lacking. This work presents a comprehensive survey, proposing a triple-level classification method to categorize, summarize, and compare existing efforts, particularly focusing on their implications for parallel architectures and distributed systems. We identify promising future directions for GNN characterization that align with the challenges of optimizing hardware and software in parallel and distributed systems. Our survey aims to help scholars systematically understand GNN performance bottlenecks and execution patterns from a computer architecture perspective, thereby contributing to the development of more efficient GNN implementations across diverse parallel architectures and distributed systems.