Time and Space Efficient Optimal Pairwise Sequence Alignment using GPU

Abstract

Sequence alignments are currently gaining close attention due to their great impact on the quality aspects of life such as facilitating early disease diagnosis, identifying the characteristics of a newly discovered sequence, etc.. With the rapid growth of genomic data, searching for a sequence homology over huge databases is unable to produce results within a realistic time. Hence, it demands an efficient sequence alignment accelerator to improve the performance of the system. Though some popular acceleration platforms, like supercomputers, Very Large Scale Integration (VLSI) chip, Graphics Processing Unit (GPUs) and Field Programmable Gate Arrays (FPGAs) based devices are available, but they are unable to meet the intended requirement to the current growth of genome database. This paper presents a parallel dynamic approach for global pairwise alignment algorithm using GPU to handle such large database. The approach performs the alignment procedure in CUDA-enabled GPU platform by creating dynamic threads to perform parallel execution. This GPU-based implementation is tested using pseudorandom database and various length of sequences. The execution result shows reasonably better performance in terms of time and space requirement with respect to existing GPU-based optimal pairwise sequence alignment approaches.