Title
A GraphBLAS Approach for Subgraph Counting.
Abstract
Subgraph counting aims to count the occurrences of a subgraph template T in a given network G. The basic problem of computing structural properties such as counting triangles and other subgraphs has found applications in diverse domains. Recent biological, social, cybersecurity and sensor network applications have motivated solving such problems on massive networks with billions of vertices. The larger subgraph problem is known to be memory bounded and computationally challenging to scale; the complexity grows both as a function of T and G. In this paper, we study the non-induced tree subgraph counting problem, propose a novel layered softwarehardware co-design approach, and implement a shared-memory multi-threaded algorithm: 1) reducing the complexity of the parallel color-coding algorithm by identifying and pruning redundant graph traversal; 2) achieving a fully-vectorized implementation upon linear algebra kernels inspired by GraphBLAS, which significantly improves cache usage and maximizes memory bandwidth utilization. Experiments show that our implementation improves the overall performance over the state-of-the-art work by orders of magnitude and up to 660x for subgraph templates with size over 12 on a dual-socket Intel(R) Xeon(R) Platinum 8160 server. We believe our approach using GraphBLAS with optimized sparse linear algebra can be applied to other massive subgraph counting problems and emerging high-memory bandwidth hardware architectures.
Year
Venue
DocType
2019
arXiv: Distributed, Parallel, and Cluster Computing
Journal
Volume
Citations 
PageRank 
abs/1903.04395
0
0.34
References 
Authors
29
10
Name
Order
Citations
PageRank
Langshi Chen111.70
Jiayu Li201.01
Ariful Azad322.42
Lei Jiang441225.59
Madhav Marathe52775262.17
Anil Kumar S. Vullikanti6113598.30
Andrey Nikolaev701.01
Egor Smirnov800.34
Ruslan Israfilov900.68
Judy Qiu1074343.25