Mastiff

Abstract

The Minimum Spanning Forest (MSF) problem finds usage in many different applications. While theoretical analysis shows that linear-time solutions exist, in practice, parallel MSF algorithms remain computationally demanding due to the continuously increasing size of data sets.In this paper, we study the MSF algorithm from the perspective of graph structure and investigate the implications of the power-law degree distribution of real-world graphs on this algorithm.We introduce the MASTIFF algorithm as a structureaware MSF algorithm that optimizes work efficiency by (1) dynamically tracking the largest forest component of each graph component and exempting them from processing, and (2) by avoiding topology-related operations such as relabeling and merging neighbour lists.The evaluations on 2 different processor architectures with up to 128 cores and on graphs of up to 124 billion edges, shows that Mastiff is 3.4-5.9× faster than previous works.

Engineering Massively Parallel MST Algorithms

“…We are also not aware of an analysis comparable to ours. Very recently, Esfahani et al [17] proposed a structure-aware algorithm that outperforms the previous shared-memory state-of-the-art MST algorithm [14] for graphs with skewed degree distributions. We discuss their results in Section VII.…”

“…For their algorithm MASTIFF, Esfahani et al [17] provide measurements on a 128-core shared-memory server with 2 TB main memory for twitter, friendster, US-road and wdc-14. Comparing their running times for the first three graphs with the fastest of our algorithms for each graph on 256 cores (6 compute nodes with a total of 576 GB main memory) yields an average speedup of MASTIFF over our algorithms of 2.5.…”

Locality Analysis of Graph Reordering Algorithms

“…(2) A wide range of real-world datasets facilitates crossdomain evaluation of the new contributions and provides broad and correct assessment across a variety of use cases (i.e., better pruning of the falsifiable insights [24]). Also, we will have the opportunity to improve several graph algorithms and optimizations that exploit the structure of graphs [2], [10], [11], [25], [26].…”