PNNL

Abstract

Weighted matching identifies a maximal subset of edges in a graph such that these edges do not share any vertices in common with each other. As a prototypical graph problem, matching has numerous applications in science and engineering, such as linear algebra, multi-level graph algorithms, computer vision and machine learning. There is a critical need for efficient matching algorithms. However, there are challenges in developing efficient, parallel graph matching methods on contemporary GPGPU systems, due to common complexities in general graph processing, such as irregular memory access patterns and load imbalances. Furthermore, increasingly massive graph sizes and resultant intermediate data commonly exceeds available GPU memory. Although dense-GPU systems are mainstream and offer accelerated on-node interconnection to enhance data access bandwidth, data dependencies and device synchronization costs in multi-GPU enabled massive-graph processing create challenges to sustainable scalability. Considering these challenges, we present efficient approximation algorithms for locally dominant matching, and we demonstrate scalability via batching and distributing graph data across multiple NVIDIA A100/V100 GPUs of NVIDIA DGX denseGPU platforms.