PFAC, the Parallel Failureless Aho-Corasick algorithm is a variant of the well-known Aho-Corasick (AC) algorithm with all failure transitions removed. The purpose of PFAC is to match all longest patterns in a given input stream against patterns pre-defined by users. The data-parallel nature of PFAC makes it perform well on GPUs, especially NVIDIA Fermi-based GPUs. The PFAC library, implemented in CUDA, provides a C level API that is easy to use. Users need not know CUDA programming. The user guide provides simple example to make it easy to use PFAC for content searches or virus detection on the GPU.

The PFAC library does not use multiple GPUs intrinsically but users can combine PFAC library with OpenMP or PThreads libraries to perform string matching on Multiple GPUs. The PFAC release includes OpenMP and PThreads examples. Download and further information: http://code.google.com/p/pfac/