Abstract
Interest has recently grown in efficiently analyzing unstructured data such as social network graphs and protein structures. A fundamental graph algorithm for doing such task is the Breadth-First Search (BFS) algorithm, the foundation for many other important graph algorithms such as calculating the shortest path or finding the maximum flow in graphs. In this paper, we share our experience of designing and implementing the BFS algorithm on Sunway TaihuLight, a newly released machine with 40,960 nodes and 10.6 million accelerator cores. It tops the Top500 list of June 2016 with a 93.01 petaflops Linpack performance [1]. Designed for extremely large-scale computation and power efficiency, processors on Sunway TaihuLight employ a unique heterogeneous many-core architecture and memory hierarchy. With its extremely large size, the machine provides both opportunities and challenges for implementing high-performance irregular algorithms, such as BFS. We propose several techniques, including pipelined module mapping, contention-free data shuffling, and group-based message batching, to address the challenges of efficiently utilizing the features of this large scale heterogeneous machine. We ultimately achieved 23755.7 giga-traversed edges per second (GTEPS), which is the best among heterogeneous machines and the second overall in the Graph500s June 2016 list [2].
Wenguang Chen
Professor
(教授)
