Scalable and efficient graph traversal on high-throughput cluster

Graph is one of the most important data structures in modern big data applications and is widely used in various fields. Among many graph algorithms, the Breadth-First Search (BFS) algorithm is a classic algorithm to solve the graph traversal problem and also the key kernel of Graph500 benchmark. On modern CPU architecture, the implementation of graph traversal on single-node systems has achieved significant improvement. However, due to the low resource utilization and high communications overhead, graph traversal on distributed clusters suffers from poor performance and energy inefficiency. High-throughput cluster (HTCs) adopt High-Throughput many-core architecture, which has the characteristics of high concurrency, strong real-time, and low-power consumption. In this work, we propose several techniques, including asynchronous virtual ring method, thread caching scheme and vertex ID reordering to solve above problems and improve BFS performance on HTCs. We systematically evaluate optimized BFS algorithm and achieve 249.74 giga-traversed edges per second (GTEPS) on 72 nodes (2880 cores) HTCs. Compared with results on Graph500 list, the optimized algorithm achieves the highest node efficiency under the same cluster scale and the performance shows weakly linear scalability as the number of cluster nodes increases. With regard to efficiency, the average performance on HTCs is 3.47 GTEPS/node, which is the best among CPU-based distributed systems on the November 2019 Graph500 list.