Frog: Asynchronous Graph Processing on GPU with Hybrid Coloring Model

• Published in: IEEE transactions on knowledge and data engineering 2018-01, Vol.30 (1), p.29-42
• Main Authors: Shi, Xuanhua, Luo, Xuan, Liang, Junling, Zhao, Peng, Di, Sheng, He, Bingsheng, Jin, Hai
• Format: Article
• Language: English
• Subjects: Acceleration; Algorithms; Apexes; asynchronous computing model; Computation; Computational modeling; Consistency; Data transfer; Datasets; GPGPU; Graph coloring; graph processing; Graph theory; Graphics processing units; Image color analysis; Iterative methods; Kernel; Locking; Parallel processing; Partitions; Search engines; Weight reduction
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2017.2745562

GPUs have been increasingly used to accelerate graph processing for complicated computational problems regarding graph theory. Many parallel graph algorithms adopt the asynchronous computing model to accelerate the iterative convergence. Unfortunately, the consistent asynchronous computing requires locking or atomic operations, leading to significant penalties/overheads when implemented on GPUs. As such, the coloring algorithm is adopted to separate the vertices with potential updating conflicts, guaranteeing the consistency/correctness of the parallel processing. Common coloring algorithms, however, may suffer from low parallelism because of a large number of colors generally required for processing a large-scale graph with billions of vertices. We propose a light-weight asynchronous processing framework called Frog with a preprocessing/hybrid coloring model. The fundamental idea is based on the Pareto principle (or 80-20 rule) about coloring algorithms as we observed through masses of real-world graph coloring cases. We find that a majority of vertices (about 80 percent) are colored with only a few colors, such that they can be read and updated in a very high degree of parallelism without violating the sequential consistency. Accordingly, our solution separates the processing of the vertices based on the distribution of colors. In this work, we mainly answer three questions: (1) how to partition the vertices in a sparse graph with maximized parallelism, (2) how to process large-scale graphs that cannot fit into GPU memory, and (3) how to reduce the overhead of data transfers on PCIe while processing each partition. We conduct experiments on real-world data (Amazon, DBLP, YouTube, RoadNet-CA, WikiTalk, and Twitter) to evaluate our approach and make comparisons with well-known non-preprocessed (such as Totem, Medusa, MapGraph, and Gunrock) and preprocessed (Cusha) approaches, by testing four classical algorithms (BFS, PageRank, SSSP, and CC). On all the tested applications and datasets, Frog is able to significantly outperform existing GPU-based graph processing systems except Gunrock and MapGraph. MapGraph gets better performance than Frog when running BFS on RoadNet-CA. The comparison between Gunrock and Frog is inconclusive. Frog can outperform Gunrock more than 1.04X when running PageRank and SSSP, while the advantage of Frog is not obvious when running BFS and CC on some datasets especially for RoadNet-CA.