Efficient Optimization of Graph Computing on High-Throughput Computer

With the rapid development of computing technology, the scale of graph increases explosively and large-scale graph computing has been the focus in recent years. Breadth first search (BFS) is a classic algorithm to solve graph traverse problem. It is the main kernel of Graph500 benchmark that evaluates the performance of supercomputers and servers in terms of data-intensive applications. High-throughput computer (HTC) adopts ARM-based many-core architecture, which has the characteristics of high concurrency, strong real-time, low-power consumption. The optimization of BFS algorithm has made a series of progress on single-node systems. In this paper, we first introduce parallel BFS algorithm and existing optimizations. Then we propose two optimization techniques for HTC to improve the efficiency of data access and data locality. We systematically evaluate the performance of BFS algorithm on HTC. For the Kronecker graph with 2scale=230whose vertices are 230 and edges are 234, the average performance on HTC is 24.26 GTEPS and 1.18 times faster than the two-way x86 server. In terms of energy efficiency, the result on HTC is 181.04 MTEPS/W and rank 2nd place on the June 2019 Green Graph500 big data list. To our best knowledge, this is the first work that evaluates BFS performance on HTC platform. HTC is suitable for data intensive applications such as large-scale graph computing.