GPU-friendly parallel genome matching with tiled access and reduced state transition table

In this paper, we propose a new parallel genome matching algorithm using graphics processing units (GPUs). Our proposed approach is based on the Aho-Corasick algorithm and it was developed based on a consideration of the architectural features of existing GPUs with a hundred or more cores. Thus, we provide an appropriate task partitioning method that runs on multiple threads and we fully utilize the cache memory and the shared memory structures available in GPUs. Especially, we propose a tiled access method for rapid data transfer from the global memory to the shared memory. We also provide new models for cache-friendly state transition table to improve performance of pattern matching operations on GPUs. The maximum throughput we achieved in various experiments was 15.3 Gbps. Moreover, we showed that our proposed design outperformed an earlier approach with a 15.4 % performance improvement.