GraLoc: Preserving Graph Locality to Minimize Read and Write Amplification on Flash Memory
Po-Lun Wang, Tseng‐Yi Chen · 2025
Due to the prevalence of social network applications and graph database systems, large-scale graphs have become a mainstream data structure for data mining and machine learning. However, the challenge arises from the limited size of dynamic random-access memory (DRAM), preventing the simultaneous processing of entire large-scale graphs in modern computer systems. Consequently, the need arises to partition the overall large-scale graph into multiple sub-graphs for parallel processing and to align with available computing resources. Unfortunately, existing graph partitioning solutions do not adequately address the load/store overhead on the storage device during the partitioning process. As the graph partitioning process involves finding the optimal splitting edge to ensure sub-graph balance and preserve the original properties of the large-scale graph, there is frequent loading from and storing to the storage device. This issue is further exacerbated on NAND flash memory due to its basic read/write unit (i.e., a page size). This paper introduces GraLoc, a graph partitioning solution designed to be NAND flash memory-friendly by considering graph locality during data placement. The proposed solution aims to maximize graph locality within a single page, effectively minimizing read amplification during the graph partitioning process. Our experiments demonstrate a significant improvement in storage performance during graph partitioning.