Accelerating the performance of Sequence Alignment using High Performance Multicore GPU

The computation grows quadratically with increase in sequences that needs to be aligned. The sequence alignment needs improvement in execution speed with the increase in database size. Dynamic programming approach if applied to search databases then it can be computationally expensive so it needs hardware-based acceleration like GPU which helps in searching databases quickly. Instead of using single threaded CPU based computing if computing capability of CPU and GPU are combined together then this hybrid approach results in much faster computation than the sequential approach. This improvement utilizes the parallel architecture of GPU for high performance computing. This improved strategy outperforms single threaded CPU based computing. Both performances are compared with different lengths of sequences. This paper also compares the parallel strategy by comparing different types of available GPU, types of memory access and different types of alignment algorithms on different benchmarks.