I/O Efficient Algorithms for Serial and Parallel Suffix Tree Construction

摘要

Over the past three decades, the suffix tree has served as a fundamental data structure in string processing. However, its widespread applicability has been hindered due to the fact that suffix tree construction does not scale well with the size of the input string. With advances in data collection and storage technologies, large strings have become ubiquitous, especially across emerging applications involving text, time series, and biological sequence data. To benefit from these advances, it is imperative that we have a scalable suffix tree construction algorithm. The past few years have seen the emergence of several disk-based suffix tree construction algorithms. However, construction times continue to be daunting-for example, indexing the entire human genome still takes over 30 hours on a system with 2 gigabytes of physical memory. In this article, we will empirically demonstrate and argue that all existing suffix tree construction algorithms have a severe limitation-to glean reasonable disk I/O efficiency, the input string being indexed must fit in main memory. This limitation is attributed to the poor locality exhibited by existing suffix tree construction algorithms and inhibits both sequential and parallel scalability. To deal with this limitation, we will show that through careful algorithm design, one of the simplest suffix tree construction algorithms can be rearchitected to build a suffix tree in a tiled manner, allowing the execution to operate within a fixed main memory budget when indexing strings of any size. We will also present a parallel extension of our algorithm that is designed for massively parallel systems like the IBM Blue Gene. An experimental evaluation will show that the proposed approach affords an improvement of several orders of magnitude in serial performance when indexing large strings. Furthermore, the proposed parallel extension is shown to be scalable-it is now possible to index the entire human genome on a 1024 processor IBM Blue Gene system in under 15 minutes.