학술논문

MTMap: A Long-Read Alignment Tool based on Multi-Core DSPs
Document Type
Conference
Source
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :863-866 Dec, 2023
Subject
Bioengineering
Computing and Processing
Engineering Profession
Robotics and Control Systems
Signal Processing and Analysis
Sequential analysis
Data analysis
Error analysis
Clustering algorithms
Genomics
Parallel processing
Data structures
Long-read alignment
Multilevel parallel
Heterogeneous processor
Vectorization
Language
ISSN
2156-1133
Abstract
Read alignment is a basic and important task in genomic data analysis. The popularity of the third—generation sequencing technology has brought the need of sequence alignment algorithms to analyze long-read sequences with longer read length and high error rate. Moreover, the rapid growth of sequence data has also presented challenges for read alignment. To improve the ability to process large volume of sequencing reads, we developed a long-read sequence alignment algorithm MTMap on the heterogeneous processor FT-m7032. MTMap utilizes multi-level parallel technologies: firstly, we tailored the data structure for the wide vector processing units of DSP to speedup the score matrix computation. Secondly, we developed multithread parallelization for base-level alignment on each DSP cluster. Finally, we implemented multi-process parallelization between DSP clusters to fully exploit the computing power of FT-m7032. Experiments show that, MTMap achieves up to 16 times of parallel acceleration performance compared with the original algorithm under the condition of ensuring accuracy.