Anchor First, Accelerate Next: Revolutionizing GNNs with PIM by Harnessing Stationary Data
Jiaxian Chen, Yue Qi, Yongbiao Zhu, Jianan Yuan, Kaoyi Sun, Tianyu Wang, Chenlin Ma, Yi Wang · 2025
Substantial data movement caused by irregular graph topologies hinders the efficient processing of graph neural networks (GNNs). Although the emerging near-bank processing-in-memory (PIM) architecture offers a promising solution to reduce data transfer between memory and computing units, cross-bank communication remains a critical challenge, limiting the benefits of PIM architectures. Our findings indicate that only $35.6 \%$ of the data can stay stationary within PIM units on average, with the rest requiring movement due to graph dependencies. This situation worsens as the number of PIM units increases, reducing the ratio to $18.7 \%$. In this paper, we argue that to fully leverage PIM architectures, systems must maximize stationary data and minimize the movement of non-stationary data. Following this principle, we propose Anchor, a scalable PIM architecture that exploits stationary data for GNNs through a hardware-software co-design approach. To maximize stationary data, we introduce the graph partitioning algorithm Mastav, which carefully allocates vertices and edges to preserve data locality. To minimize the movement of non-stationary data, we employ a two-step strategy. First, a customized dataflow ensures that non-stationary data is accessed and distributed exactly once. Second, an optimized communication mechanism reduces redundant data transfers through critical paths. Our extensive experiments demonstrate that Anchor significantly reduces processing latency and data movement compared to representative schemes.