The advent of Graph Neural Networks (GNNs) has revolutionized the field of
machine learning, offering a novel paradigm for learning on graph-structured
data. Unlike traditional neural networks, GNNs are capable of capturing complex
relationships and dependencies inherent in graph data, making them particularly
suited for a wide range of applications including social network analysis,
molecular chemistry, and network security. The impact of GNNs in these domains
is profound, enabling more accurate models and predictions, and thereby
contributing significantly to advancements in these fields.
GNNs, with their unique structure and operation, present new computational
challenges compared to conventional neural networks. This requires
comprehensive benchmarking and a thorough characterization of GNNs to obtain
insight into their computational requirements and to identify potential
performance bottlenecks. In this thesis, we aim to develop a better
understanding of how GNNs interact with the underlying hardware and will
leverage this knowledge as we design specialized accelerators and develop new
optimizations, leading to more efficient and faster GNN computations.
Synthesizing these insights and optimizations, we design a state-of-the-art
hardware accelerator capable of efficiently handling various GNN workloads. Our
accelerator architecture is built on our characterization of GNN computational
demands, providing clear motivation for our approach. Furthermore, we extend
our exploration to emerging GNN workloads in the domain of graph neural
networks. This exploration into novel models underlines our comprehensive
approach, as we strive to enable accelerators that are not just performant, but
also versatile, able to adapt to the evolving landscape of graph computing.

图神经网络在图结构数据上学习提供了一种新颖的机器学习范例，能够捕捉图数据中的复杂关系和依赖性，从而在社交网络分析、分子化学和网络安全等领域具有重要的影响。本论文旨在深入研究 GNN 与底层硬件的交互方式，并设计专用加速器和新型优化方法，以实现更高效和更快速的 GNN 计算。我们基于对 GNN 计算需求的特征化构建了先进的硬件加速器结构，同时扩展了对图神经网络领域中新型模型的探索，旨在提供不仅高性能而且能适应图计算发展的多功能加速器。

图计算加速器的实现

Enabling Accelerators for Graph Computing

Traffic simulation is a crucial tool for transportation decision-making and
policy development. However, achieving realistic simulations in the face of the
high dimensionality and heterogeneity of traffic environments is a longstanding
challenge. In this paper, we present TransWordNG, a traffic simulator that uses
Data-driven algorithms and Graph Computing techniques to learn traffic dynamics
from real data. The functionality and structure of TransWorldNG are introduced,
which utilize a foundation model for transportation management and control. The
results demonstrate that TransWorldNG can generate more realistic traffic
patterns compared to traditional simulators. Additionally, TransWorldNG
exhibits better scalability, as it shows linear growth in computation time as
the scenario scale increases. To the best of our knowledge, this is the first
traffic simulator that can automatically learn traffic patterns from real-world
data and efficiently generate accurate and realistic traffic environments.

TransWordNG 是第一款能够从真实交通数据中自动学习交通模式、高效生成准确、逼真交通环境的交通模拟器，它使用数据驱动的算法和图计算技术来应对交通环境的高维度和异质性。