Graph processing is increasingly important in numerous domains, including recommender systems, neuroscience, cybersecurity, and social network analysis (Wu et al., 2023; Bullmore & Sporns, 2009; Wang et al., 2019; Yin et al., 2023; Luo et al., 2023; He et al., 2024). However, the unique characteristics of graph data, such as irregularity and unstructuredness, pose significant challenges to achieving high performance. This paper explores the latest advancements in hardware and software co-design techniques aimed at addressing these challenges and improving the efficiency of graph processing systems. We examine novel architectural approaches, memory management strategies, and software frameworks that collectively contribute to enhanced performance.