SWIFT-a performance accelerated optimized string matching algorithm for NVIDIA GPUs

This paper presents a study of exact string matching algorithms and their performance behavior when executed on dynamic parallelism enabled Kepler Graphics Processing Unit (GPU) by Nvidia. The algorithms considered in this paper are Quick search (QS), Horspool (HP), and Brute force (BF) string matching. Their efficient implementation on Kepler gives a remarkable improvement over their respective multi-core CPU and generic GPU implementations. In addition, the proposed work further optimizes the algorithm by exploiting the fundamental architectural aspects of the GPU like the memory hierarchy, lightweight threads and avoiding strided access. The optimization associated with the memory is called Binning and the one using memory alignment is named Chunking. As a result of the significant boost obtained by extracting the most out of these features, the newer methods are named as SWIFT. SWIFT algorithms give performance benefit of about 1.7X on an average and up to 5X in some cases, which will be discussed in the paper. Also, the paper proposes a hybrid algorithm that employs both regular GPU implementation and SWIFT based on a predefined condition that gives benefit for all pattern sizes. The experimental results for different pattern sizes using benchmark datasets are presented in the paper.