Graphago: Accelerating SSD-based Graph Processing via Activity-Aware Graph Preprocessing

SSD-based graph processing systems have emerged as a cost-effective solution for handling the ever-growing, large-scale graphs that exceed the memory capacity of a single machine. However, the mismatch between the large SSD access granularity (e.g., 4KB) and the small size of the graph vertex data leads to significant read amplification and low I/O efficiency. Despite existing works proposing techniques like dynamic active data gathering or reordering-based graph preprocessing to tackle this challenge, they inevitably cause problems such as expensive on-line computation overheads, inefficient graph traversal, and I/O imbalance, thus degrading the performance of graph processing.