With the increasing complexity of graph structures, the current real-world large-scale graphs are being represented by a considerable amount of vertex and edge data. Furthermore, the analysis of a large number of computing nodes has become a very complicated job that requires a large amount of hardware resources. Moreover, in large-scale graph processing, the vertex and edge data show random and sequential memory access patterns at the same time, and this is a major bottleneck in graph processing. In this paper, we present a high-capacity main memory system with an intelligent pattern-aware prefetching engine to overcome the scalability problem and the memory inefficiency of single-machine graph processing. The proposed intelligent pattern-aware prefetching engine is designed to predict and handle sequential or regular patterns and random-access patterns simultaneously. Experimental results demonstrated that the proposed model exhibited performance improvements of 60% over conventional DRAM models, approximately 40% over the existing prefetch models, and about 12.5% over the latest prefetch models.

随着图结构的复杂性增加，当前的现实世界大型图由大量的顶点和边数据表示。此外，对大量计算节点的分析已成为一项非常复杂的工作，需要大量的硬件资源。此外，在大规模图形处理中，顶点和边缘数据同时显示随机和顺序的内存访问模式，这是图形处理的主要瓶颈。在本文中，我们提出了一种具有智能模式感知预取引擎的大容量主存储系统，以克服可扩展性问题和单机图处理的存储效率低下的问题。提出的智能模式感知预取引擎旨在同时预测和处理顺序或常规模式以及随机访问模式。实验结果表明，所提出的模型与常规DRAM模型相比，性能提高了60％，比现有的预取模型提高了约40％，而最新的预取模型提高了约12.5％。