Parallel DNA Sequence Alignment on High-Performance Systems with CUDA and MPI
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913629399416832 |
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| author | Zwaka, Linus |
| author_facet | Zwaka, Linus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_21103 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Parallel DNA Sequence Alignment on High-Performance Systems with CUDA and MPI Zwaka, Linus Distributed, Parallel, and Cluster Computing 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. |
| title | Parallel DNA Sequence Alignment on High-Performance Systems with CUDA and MPI |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2412.21103 |