Graphs are used to store relationships on a variety of topics, such as road map data and social media connections. Processing this data allows one to uncover insights from its structure. However, while analyzing graphs with traditional processors, the graph connectivity can result in irregular memory access patterns and thus poor data locality that can result in low performance. Processing-in-Memory (PIM) is an attractive alternative for graph processing, as it can reduce data movement by bringing the computation closer to the data itself. While PIM-based techniques have been shown to improve graph processing performance, there is still room for improvement, as critical bottlenecks exist when connecting multiple PIM-based accelerators into larger clusters. Although a number of recent proposals have aimed to reduce inter-accelerator data movement, their techniques have generally overlooked the potential to optimize how the graph’s connectivity can lead to a more efficient hardware mapping. In fact, many real-world graphs have a small percentage of high-degree nodes that connect widely to a large number of other nodes. By clustering these nodes into communities, one can more efficiently map them to hardware, minimizing expensive inter-accelerator communication, a key performance bottleneck in these accelerators. To capitalize on this observation, we propose PIM-GraphSCC, the first PIM-based graph processor that exploits a graph’s connectivity to significantly reduce communication over critical resources: the inter-accelerator links. By partitioning graphs into communities, PIM-GraphSCC provides a community-aware graph partitioning scheme that reduces inter-accelerator data movement by up to 93 percent compared to modern graph processing schemes.

中文翻译：

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PIM-GraphSCC：使用图的社区结构进行基于 PIM 的图处理

图用于存储各种主题的关系，例如路线图数据和社交媒体连接。处理这些数据可以让人们从其结构中发现见解。然而，在使用传统处理器分析图形时，图形连通性会导致不规则的内存访问模式，从而导致数据局部性差，从而导致性能低下。内存处理 (PIM) 是图形处理的一种有吸引力的替代方案，因为它可以通过使计算更接近数据本身来减少数据移动。虽然基于 PIM 的技术已被证明可以提高图形处理性能，但仍有改进的空间，因为将多个基于 PIM 的加速器连接到更大的集群时存在关键瓶颈。尽管最近的一些提议旨在减少加速器间的数据移动，他们的技术通常忽略了优化图形连接如何导致更有效的硬件映射的潜力。事实上，许多现实世界的图都有一小部分高度节点，这些节点广泛地连接到大量其他节点。通过将这些节点聚集到社区中，人们可以更有效地将它们映射到硬件，从而最大限度地减少昂贵的加速器间通信，这是这些加速器的一个关键性能瓶颈。为了利用这一观察结果，我们提出了 PIM-GraphSCC，这是第一个基于 PIM 的图形处理器，它利用图形的连接性来显着减少关键资源的通信：加速器间链接。通过将图划分为社区，