.: Smith-Waterman CUDA Source
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
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.
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.
source code (SmithWatermanWindows.zip)
information about the Smith-Waterman project