GPregel: A GPU-Based Parallel Graph Processing Model

With the development of information technology, graph computing becomes an increasingly important tool for information processing. Recently, GPU has been adopted to accelerate various graph processing algorithms. However, since the architecture of GPU is very different from traditional computing model, the learning threshold for developing GPU-based applications is high. In this paper, we propose a GPU-based parallel graph processing system named GPregel. GPregel harnesses a lightweight complier to hide the underlying complexity of the parallel details and simplifies programming. It greatly reduces the difficulty in utilizing the GPU to solve graph computing problems. We also design a special storage model for BSP model running on GPU, which overcomes the execution divergence and irregular memory access by coarse-grained designs. Experiments demonstrate that GPregel can achieve high performance with little work for developers.