Presentation
Graphago: Accelerating SSD-Based Graph Processing via Activity-Aware Graph Preprocessing
DescriptionSSD-based graph processing systems have emerged as a cost-effective solution for handling large-scale graphs. However, the large access granularity (e.g., 4KB) of an SSD often leads to low I/O efficiency. In this paper, we propose Graphago, an activity-aware graph preprocessing technique for SSD-based graph processing systems. The main idea of Graphago is the combined use of three key designs that synergistically optimize the graph storage and organization based on the active extent of graph data, thereby achieving both high I/O efficiency and satisfactory processing performance: 1) a dual-centrality activity prediction model to efficiently predict the active extent of each vertex, 2) an activity-neighborhood graph ordering technique to minimize read amplification without sacrificing graph traversal efficiency, and 3) an active-data-balanced graph partitioning scheme to address the I/O imbalance problem. Our evaluation results show that Graphago outperforms state-of-the-art SSD-based graph processing systems by up to 4.8×.
Event Type
Paper
TimeWednesday, 19 November 20254:15pm - 4:37pm CST
Location275
Data Analytics, Visualization & Storage