Trie compression for GPU accelerated multi-pattern matching
Item TypeConference Paper
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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.
Bellekens, X. et al. 2017. Trie compression for GPU accelerated multi-pattern matching. In: H. Manaert et al , eds. Proceedings of the 9th International Conferences on Pervasive Patterns and Applications Athens, Greece. 19-23 February 2017.pp. 93-96.