A Block-Based Triangle Counting Algorithm on Heterogeneous Environments
- Georgia Institute of Technology, Atlanta, GA (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Triangle counting is a fundamental building block in graph algorithms. In this paper, we propose a block-based triangle counting algorithm to reduce data movement during both sequential and parallel execution. Our block-based formulation makes the algorithm naturally suitable for heterogeneous architectures. The problem of partitioning the adjacency matrix of a graph is well-studied. Our task decomposition goes one step further: it partitions the set of triangles in the graph. By streaming these small tasks to compute resources, we can solve problems that do not fit on a device. We demonstrate the effectiveness of our approach by providing an implementation on a compute node with multiple sockets, cores and GPUs. The current state-of-the-art in triangle enumeration processes the Friendster graph in 2.1 seconds, not including data copy time between CPU and GPU. Using that metric, our approach is 20 percent faster. When copy times are included, our algorithm takes 3.2 seconds. This is 5.6 times faster than the fastest published CPU-only time.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Georgia Institute of Technology, Atlanta, GA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1669197
- Report Number(s):
- SAND--2020-10333R; 690948
- Country of Publication:
- United States
- Language:
- English
Similar Records
Trust: Triangle Counting Reloaded on GPUs
Wedge sampling for computing clustering coefficients and triangle counts on large graphs