Trie Compression for GPU Accelerated Multi-Pattern Matching

Abstract

Graphics Processing Units 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.

4 pages, 6 figures. Accepted and Published in The Ninth International Conferences on Pervasive Patterns and Applications PATTERNS 2017 (19 - 23/02, 2017 - Athens, Greece)