Accelerating global sequence alignment using CUDA compatible multi-core GPU

The Graphical Processing Unit (GPU) has become a competitive general purpose computational hardware platform in the last few years. Recent improvements in GPUs highly parallel programming capabilities such as Compute Unified Device Architecture(CUDA) has lead to a variety of complex applications with tremendous performance improvements. Genetic Sequence alignment is considered to be one of the application domains which require further improvements in the execution speed, because it is a computationally intensive task with increased database size. We focus on using the massively parallel architecture of GPU as a solution for the improvement of sequence alignment task. For that purpose we have implemented a CUDA based heterogeneous solution for the global sequence alignment task with Needleman-Wunsch dynamic programming algorithm. We have compared different levels of memory access patterns to identify better parallelization strategy with different ways of kernel access and thread utilization methods.