A comparison of FPGAs, GPUS and CPUS for Smith-Waterman algorithm (abstract only).

The Smith-Waterman algorithm is a key technique for comparing genetic sequences. This paper presents a comprehensive study of a systolic design for Smith-Waterman algorithm. It is parameterized in terms of the sequence length, the amount of parallelism, and the number of FPGAs. Two methods of organizing the parallelism, the line-based and the lattice-based methods, are introduced. Our analytical treatment reveals how these two methods perform relative to peak performance when the level of parallelism varies. A novel systolic design is then described, showing how the parametric description can be effectively implemented, with specific focus on enhancing parallelism and on optimizing the total size of memory and circuits; in particular, we develop efficient realizations for compressing score matrices and for reducing affine gap cost functions. Promising results have been achieved showing, for example, a single XC5VLX330 FPGA at 131MHz can be three times faster than a platform with two NVIDIA GTX295 at 1242MHz.