Graph mining applications, such as subgraph pattern matching and mining, are widely used in real-world domains such as bioinformatics, social network analysis, and computer vision. Such applications are considered a new class of data-intensive applications that generate massive irregular computation workloads and memory accesses, which degrade the performance significantly. Leveraging emerging hardware, such as process-in-memory (PIM) technology, could potentially accelerate such applications. In this paper, we propose PIMMiner, a high-performance PIM architecture graph mining framework. We first identify that current PIM architecture cannot be fully utilized by graph mining applications. Next, we propose a set of optimizations and interfaces that enhance the locality, and internal bandwidth utilization and reduce remote bank accesses and load imbalance through cohesive algorithm and architecture co-designs. We compare PIMMiner with several state-of-the-art graph mining frameworks and show that PIMMiner is able to outperform all of them significantly.

翻译：图挖掘应用（如子图模式匹配与挖掘）广泛应用于生物信息学、社交网络分析和计算机视觉等现实领域。这类应用被视为新型数据密集型应用，会产生大量不规则计算负载和内存访问，导致性能显著下降。利用新兴硬件（如存内处理技术）可能加速此类应用。本文提出PIMMiner——一种高性能PIM架构图挖掘框架。我们首先指出现有PIM架构无法被图挖掘应用充分利用。接着，通过算法与架构协同设计，提出一系列优化方案与接口，以增强局部性与内部带宽利用率，减少远程存储体访问与负载不均衡问题。我们将PIMMiner与多个最先进的图挖掘框架进行对比，结果表明PIMMiner能够显著超越所有对比框架。