Parallel DNA Sequence Alignment on High-Performance Systems with CUDA and MPI

TL;DR

This paper presents a hybrid CUDA and MPI implementation of the Needleman-Wunsch algorithm, significantly accelerating DNA sequence alignment on high-performance systems by leveraging GPU and distributed computing.

Contribution

It introduces a novel hybrid parallelization approach combining CUDA and MPI for efficient large-scale sequence alignment without altering the core algorithm.

Findings

Achieves significant speedup over CPU-based methods

Effective handling of large datasets and multiple alignments

Demonstrates scalability on supercomputing systems

Abstract

Sequence alignment is a cornerstone of bioinformatics, widely used to identify similarities between DNA, RNA, and protein sequences and studying evolutionary relationships and functional properties. The Needleman-Wunsch algorithm remains a robust and accurate method for global sequence alignment. However, its computational complexity, O(mn), poses significant challenges when processing large-scale datasets or performing multiple sequence alignments. To address these limitations, a hybrid implementation of the Needleman-Wunsch algorithm that leverages CUDA for parallel execution on GPUs and MPI for distributed computation across multiple nodes on a supercomputer is proposed. CUDA efficiently offloads computationally intensive tasks to GPU cores, while MPI enables communication and workload distribution across nodes to handle large-scale alignments. This work details the implementation and performance evaluation of the Needleman-Wunsch algorithm in a massively parallel computing environment. Experimental results demonstrate significant acceleration of the alignment process compared to traditional CPU-based implementations, particularly for large input sizes and multiple sequence alignments. In summary, the combination of CUDA and MPI effectively overcomes the computational bottlenecks inherent to the Needleman-Wunsch algorithm without requiring substantial modifications to the underlying algorithm, highlighting the potential of high-performance computing in advancing sequence alignment workflows.