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Hardware acceleration of genomics data analysis: challenges and opportunities
Tony Robinson1, Jim Harkin1, Priyank Shukla2
1School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry/Londonderry, BT48 7JL, UK.
Bioinformatics (Oxford, England)
|May 26, 2021
Summary
Networks-On-Chip (NoC) offer a scalable solution for accelerating short read alignment (SRA) in bioinformatics. This technology minimizes memory latency, addressing key challenges in next-generation sequencing data analysis.
Area of Science:
- Bioinformatics
- Genomics
- Computer Architecture
Background:
- The decreasing cost of genome sequencing shifts computational focus to genomic data analysis.
- Short Read Alignment (SRA) is a major bottleneck in bioinformatics pipelines.
- Previous many-core approaches for SRA acceleration faced scalability and energy efficiency issues due to global memory dependence.
Purpose of the Study:
- To review current hardware acceleration strategies for genomic data analysis.
- To explore the potential of Networks-On-Chip (NoC) for accelerating SRA.
- To identify challenges and opportunities for NoCs in next-generation sequencing (NGS).
Main Methods:
- Review of existing hardware and software acceleration techniques for SRA.
- Analysis of NoC architecture for integrating computational blocks in SRA.
- Evaluation of NoC's impact on memory latency and global memory access.
Main Results:
- NoCs demonstrate potential for efficient integration of SRA computational blocks.
- NoCs minimize memory latency and global memory access compared to traditional many-core designs.
- NoCs offer improved scalability and energy efficiency for SRA.
Conclusions:
- NoCs represent a promising hardware solution for accelerating genomic data analysis.
- Utilizing NoCs is crucial for advancing the speed and efficiency of NGS technologies.
- Further research and development are needed to fully leverage NoCs in bioinformatics pipelines.
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