Program development environments have enabled graphics processing units (GPUs) to become an attractive high performance computing platform for the scientific community. A commonly posed problem in computational biology is protein database searching for functional similarities. The most accurate algorithm for sequence alignments is Smith-Waterman (SW).

This is a first of a kind implementation of Smith-Waterman which purely runs on the GPU instead of a CPU-GPU integrated environment, making the design suitable for porting onto a cluster of GPUs.

This source code implements new techniques to reduce the memory footprint of the application while exploiting the memory hierarchy of the GPU. With this implementation, GSW, we overcome the on chip memory size constraint, achieving 23x speedup in terms of clock cycles compared to the serial implementation. Our analysis show that as the query length increases our speedup almost stays stable indicating the solid scalability of our approach.

Readme (.txt) source code (SmithWatermanWindows.zip)

More information about the Smith-Waterman project here