—Graphics Processing Units (GPU) allow for running massively parallel applications offloading the Central Processing Unit (CPU) from computationally intensive resources. However GPUs have a limited amount of memory. In this paper, a trie...
more—Graphics Processing Units (GPU) allow for running massively parallel applications offloading the Central Processing Unit (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.
