MND-MST

Abstract

Efficient processing of large-scale graph applications on heterogeneous CPU-GPU systems require effectively harnessing the combined power of both the CPU and GPU devices. Finding minimum spanning tree (MST) is an important graph application and is used in different domains. When applying MST algorithms for large-scale graphs across multiple nodes (or machines), the existing approaches use BSP (bulk synchronous parallel) model involving large-scale communications. In this paper, we propose a multi-node multi-device algorithm for MST, MND-MST, that uses a divide-and-conquer approach by partitioning the input graph across multiple nodes and devices and performing independent Boruvka's MST computations on the devices. The results from the different nodes are merged using a novel hybrid merging algorithm that ensures that the combined results on a node never exceeds it memory capacity. The algorithm also simultaneously harnesses both CPU and GPU devices. In our experiments, we show that our proposed algorithm shows 24-88% performance improvements over an existing BSP approach. We also show that the algorithm exhibits almost linear scalability, and the use of GPUs result in upto 23% improvement in performance over multi-node CPU-only performance.