Abstract
Genome sequences contain hundreds of millions of DNA base pairs. Finding the degree of similarity between two genomes requires executing a compute-intensive dynamic programming algorithm, such as Smith-Waterman. Traditional von Neumann architectures have limited parallelism and cannot provide an efficient solution for large-scale genomic data. Approximate heuristic methods (e.g. BLAST) are commonly used. However, they are suboptimal and still compute-intensive. In this work, we present BioSEAL, a biological sequence alignment accelerator. BioSEAL is a massively parallel non-von Neumann processing-in-memory architecture for large-scale DNA and protein sequence alignment. BioSEAL is based on resistive content addressable memory, capable of energy-efficient and highperformance associative processing. We present an associative processing algorithm for entire database sequence alignment on BioSEAL and compare its performance and power consumption with state-of-art solutions. We show that BioSEAL can achieve up to 57× speedup and 156× better energy efficiency, compared with existing solutions for genome sequence alignment and protein sequence database search.
Original language | English |
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Title of host publication | SYSTOR 2020 - Proceedings of the 13th ACM International Systems and Storage Conference |
Publisher | Association for Computing Machinery |
Pages | 36-48 |
Number of pages | 13 |
ISBN (Electronic) | 9781450375887 |
DOIs | |
State | Published - 30 May 2020 |
Externally published | Yes |
Event | 13th ACM International Systems and Storage Conference, SYSTOR 2020 - Haifa, Israel Duration: 13 Oct 2020 → 15 Oct 2020 |
Publication series
Name | SYSTOR 2020 - Proceedings of the 13th ACM International Systems and Storage Conference |
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Conference
Conference | 13th ACM International Systems and Storage Conference, SYSTOR 2020 |
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Country/Territory | Israel |
City | Haifa |
Period | 13/10/20 → 15/10/20 |
Bibliographical note
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