Frog: Asynchronous Graph Processing on GPU with Hybrid Coloring Model

Journal Article · Tue Aug 29 00:00:00 EDT 2017 · IEEE Transactions on Knowledge and Data Engineering

DOI:https://doi.org/10.1109/TKDE.2017.2745562· OSTI ID:1416975

Shi, Xuanhua ^[1]; Luo, Xuan ^[1]; Liang, Junling ^[1]; Zhao, Peng ^[1]; Di, Sheng ^[2]; He, Bingsheng ^[3]; Jin, Hai ^[1]

---

1. Huazhong Univ. of Science and Technology, Wuhan (China). Services Computing Technology and System Lab., Big Data Technology and System Lab., School of Computer
2. Argonne National Lab. (ANL), Argonne, IL (United States)
3. National Univ. of Singapore (Singapore). Dept. of Computer Science

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, 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 Pareto principle (or 80-20 rule) about coloring algorithms as we observed through masses of realworld graph coloring cases. We find that a majority of vertices (about 80%) 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. Here, 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.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: National Natural Science Foundation of China (NNSFC); USDOE Office of Science (SC)
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 1416975
• Journal Information: IEEE Transactions on Knowledge and Data Engineering, Journal Name: IEEE Transactions on Knowledge and Data Engineering Journal Issue: 1 Vol. 30; ISSN 1041-4347
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Asynchronous Computing Model
GPGPU
Graph Processing