📝 SummarXiv

Abstract

Maximal Matching (MM) is a fundamental graph problem with diverse applications. However, state-of-the-art parallel MM algorithms are limited by their need to process graph edges repeatedly over multiple iterations. Furthermore, optimized algorithms often require additional memory for graph contraction or edge filtering. In this paper, we introduce Skipper, an incremental asynchronous MM algorithm that (i) processes each edge deterministically and only once, (ii) skips a large fraction of edges during processing, and (iii) minimizes memory space utilization. Notably, Skipper requires (a) a single pass over the edges, and (b) only a single byte of memory space per vertex. Our evaluation of Skipper, using both real-world and synthetic graphs with up to 161 billion edges, and across three different computer architectures, shows that Skipper processes only 1.2% of the edges and delivers a 47.1 times average speedup (geometric mean). Moreover, Skipper's output quality is highly competitive, with an average size of 88.6% relative to the output of the Lim-Chung algorithm as a state-of-the-art MM algorithm with the largest output size.