@inproceedings{0a8e023079114da7a14144aed5a2249d,
title = "Trie compression for GPU accelerated multi-pattern matching",
abstract = "Graphics Processing Units (GPU) allow for running massively parallel applications offloading the CPU from computationally intensive resources, however GPUs have a limited amount of memory. In this paper a trie compression algorithm for massively parallel pattern matching is presented demonstrating 85% less space requirements than the original highly efficient parallel failure-less Aho-Corasick, whilst demonstrating over 22 Gbps throughput. The algorithm presented takes advantage of compressed row storage matrices as well as shared and texture memory on the GPU.",
author = "Xavier Bellekens and Amar Seeam and Christos Tachtatzis and Robert Atkinson",
year = "2017",
month = feb,
day = "28",
language = "English",
isbn = "9781612085340",
publisher = "International Academy, Research, and Industry Association (IARIA)",
booktitle = "Proceedings of the 9th International Conferences on Pervasive Patterns and Applications",
note = "PATTERNS 2017 : The Ninth International Conferences on Pervasive Patterns and Applications ; Conference date: 19-02-2016 Through 23-02-2016",
}