Ultra Efficient Acceleration for De Novo Genome Assembly via Near-Memory Computing
De novo assembly of genomes for which there is no reference, is essential for novel species discovery and metagenomics. In this work, we accelerate two key performance bottlenecks of DBG-based assembly, graph construction and graph traversal, with a near-data processing (NDP) architecture based on 3...
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| Vydané v: | 2021 30th International Conference on Parallel Architectures and Compilation Techniques (PACT) s. 199 - 212 |
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01.09.2021
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| Abstract | De novo assembly of genomes for which there is no reference, is essential for novel species discovery and metagenomics. In this work, we accelerate two key performance bottlenecks of DBG-based assembly, graph construction and graph traversal, with a near-data processing (NDP) architecture based on 3D-stacking. The proposed framework distributes key operations across NDP cores to exploit a high degree of parallelism and high memory bandwidth. We propose several optimizations based on domain-specific properties to improve the performance of our design. We integrate the proposed techniques into an existing DBG assembly tool, and our simulation-based evaluation shows that the proposed NDP implementation can improve the performance of graph construction by 33× and traversal by 16× compared to the state-of-the-art. |
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| AbstractList | De novo assembly of genomes for which there is no reference, is essential for novel species discovery and metagenomics. In this work, we accelerate two key performance bottlenecks of DBG-based assembly, graph construction and graph traversal, with a near-data processing (NDP) architecture based on 3D-stacking. The proposed framework distributes key operations across NDP cores to exploit a high degree of parallelism and high memory bandwidth. We propose several optimizations based on domain-specific properties to improve the performance of our design. We integrate the proposed techniques into an existing DBG assembly tool, and our simulation-based evaluation shows that the proposed NDP implementation can improve the performance of graph construction by 33× and traversal by 16× compared to the state-of-the-art. |
| Author | Rosing, Tajana Moshiri, Niema Wu, Lingxi Li, Muzhou Zhou, Minxuan Skadron, Kevin |
| Author_xml | – sequence: 1 givenname: Minxuan surname: Zhou fullname: Zhou, Minxuan email: miz087@eng.ucsd.edu organization: University of California, San Diego – sequence: 2 givenname: Lingxi surname: Wu fullname: Wu, Lingxi email: lw2ef@virginia.edu organization: University of Virginia – sequence: 3 givenname: Muzhou surname: Li fullname: Li, Muzhou email: mul023@eng.ucsd.edu organization: University of California, San Diego – sequence: 4 givenname: Niema surname: Moshiri fullname: Moshiri, Niema email: niema@ucsd.edu organization: University of California, San Diego – sequence: 5 givenname: Kevin surname: Skadron fullname: Skadron, Kevin email: skadron@virginia.edu organization: University of Virginia – sequence: 6 givenname: Tajana surname: Rosing fullname: Rosing, Tajana email: tajana@ucsd.edu organization: University of California, San Diego |
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| Snippet | De novo assembly of genomes for which there is no reference, is essential for novel species discovery and metagenomics. In this work, we accelerate two key... |
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| SubjectTerms | Accelerator Design Bandwidth Bioinformatics Genomics Message passing Parallel processing Precision medicine Processing in memory Vaccines |
| Title | Ultra Efficient Acceleration for De Novo Genome Assembly via Near-Memory Computing |
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