SW-actors: accelerating the Smith-Waterman algorithm via actors.

Area of Science:

• Bioinformatics
• Computational Biology
• Algorithm Development

Background:

• The Smith-Waterman (SW) algorithm is the gold standard for local sequence alignment.
• Serial implementations of SW have limitations in processing large biological datasets due to time complexity.

Purpose of the Study:

• Introduce SW-actors, a novel parallel implementation of the SW algorithm.
• Optimize resource utilization and improve the efficiency of local sequence alignment tasks.

Main Methods:

• Leveraged the actor model of concurrent computation for parallel processing.
• Implemented task scheduling and management at interalignment and intraalignment levels.
• Compared SW-actors with state-of-the-art tools (Parasail, SeqAn, SWIPE) on diverse datasets.

Main Results:

• SW-actors demonstrated significant speedups over existing implementations, ranging from 1.33x to 2.49x.
• Achieved up to 22x speedup compared to serial execution on 40 cores.
• Showed consistent performance gains for larger datasets, proving advantageous for medium- to large-scale alignments.

Conclusions:

• SW-actors offers a highly efficient and scalable solution for local sequence alignment.
• The parallel implementation overcomes the limitations of serial SW algorithms for large-scale bioinformatics tasks.
• The actor model provides an effective framework for optimizing concurrent computation in sequence alignment.