high performance computing on graphics processing units

We develop GPU adaptations of the Aho-Corasick and multipattern Boyer-Moore string matching algorithms for the two cases GPU-to-GPU (input is initially in GPU memory and the output is left in GPU memory) and host-to-host (input and output are in the memory of the host CPU). For the GPU-to-GPU case, we consider several refinements to a base GPU implementation and measure the performance gain from each refinement. For the host-to-host case, we analyze two strategies to communicate between the host and the GPU and show that one is optimal with respect to run time while the other requires less device memory. Experiments conducted on an NVIDIA Tesla GT200 GPU that has 240 cores running off of a Xeon 2.8GHz quad-core host CPU show that, for the GPU-to-GPU case, our Aho-Corasick GPU adaptation achieves a speedup between 8.5 and 9.5 relative to a single-thread CPU implementation and between 2.4 and 3.2 relative to the best multithreaded implementation. For the host-to-host case, the GPU AC code achieves a speedup of 3.1 relative to a single-threaded CPU implementation. However, the GPU is unable to deliver any speedup relative to the best multithreaded code running on the quad-core host. In fact, the measured speedups for the latter case ranged between 0.74 and 0.83.