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Background  The Smith-Waterman (SW) algorithm is the best choice for searching similar regions between two DNA or protein sequences. However, it may become impracticable in some contexts due to its high computational demands. Consequently, the computer science community has focused on the use of modern parallel architectures such as Graphics Processing Units (GPUs), Xeon Phi accelerators and Field Programmable Gate Arrays (FGPAs) to speed up large-scale workloads.    Results  This paper presents and evaluates  SWIFOLD : a  S mith- W aterman parallel  I mplementation on  F PGA with  O penCL for  L ong  D NA sequences. First, we evaluate its performance and resource usage for different kernel configurations. Next, we carry out a performance comparison between our tool and other state-of-the-art implementations considering three different datasets.  SWIFOLD  offers the best average performance for small and medium test sets, achieving a performance that is independent of input size and sequence similarity. In addition,  SWIFOLD  provides competitive performance rates in comparison with GPU-based implementations on the latest GPU generation for the large dataset.    Conclusions  The results suggest that  SWIFOLD  can be a serious contender for accelerating the SW alignment of DNA sequences of unrestricted size in an affordable way reaching on average 125 GCUPS and almost a peak of 270 GCUPS.

SWIFOLD: Smith-Waterman implementation on FPGA with OpenCL for long DNA sequences