ParaSAT: a scalable parallel framework for sequence alignment

Sequence alignment is a fundamental step in genomic data analysis. Third-generation sequencing technology facilitates the acquisition of high-quality genomic data but the explosive growth of sequencing data poses huge challenges to current sequence alignment. To reduce sequence alignment time and enhance alignment performance, a parallelization framework based on Minimap2 is proposed, called ParaSAT, aiming to expedite sequence alignment and offer insights to researchers in the field. In order to achieve load balancing among multi-nodes, We design a task pool scheduling strategy which can dynamically distribute tasks according to the status of compute nodes. To evaluate the performance of the framework, We choose 6 dataset to conduct experiments on TH-1 supercomputer. The outcomes confirm that the parallel framework ensures sequence alignment accuracy, and demonstrates a notable speedup on large datasets, approaching linearity, with parallel efficiency consistently above 80%. The framework also exhibits strong and weak scalability, effectively enhancing the efficiency of sequence alignment and offering guidance for high-performance genomic data processing.