Accelerating Seed Location Filtering in DNA Read Mapping Using a Commercial Compute-in-SRAM Architecture
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917572142694400 |
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| author | Golden, Courtney Ilan, Dan Cebry, Nicholas Batten, Christopher |
| author_facet | Golden, Courtney Ilan, Dan Cebry, Nicholas Batten, Christopher |
| contents | DNA sequence alignment is an important workload in computational genomics. Reference-guided DNA assembly involves aligning many read sequences against candidate locations in a long reference genome. To reduce the computational load of this alignment, candidate locations can be pre-filtered using simpler alignment algorithms like edit distance. Prior work has explored accelerating filtering on simulated compute-in-DRAM, due to the massive parallelism of compute-in-memory architectures. In this paper, we present work-in-progress on accelerating filtering using a commercial compute-in-SRAM accelerator. We leverage the recently released Gemini accelerator platform from GSI Technology, which is the first, to our knowledge, commercial-scale compute-in-SRAM system. We accelerate the Myers' bit-parallel edit distance algorithm, producing average speedups of 14.1x over single-core CPU performance. Individual query/candidate alignments produce speedups of up to 24.1x. These early results suggest this novel architecture is well-suited to accelerating the filtering step of sequence-to-sequence DNA alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11685 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Seed Location Filtering in DNA Read Mapping Using a Commercial Compute-in-SRAM Architecture Golden, Courtney Ilan, Dan Cebry, Nicholas Batten, Christopher Hardware Architecture Genomics DNA sequence alignment is an important workload in computational genomics. Reference-guided DNA assembly involves aligning many read sequences against candidate locations in a long reference genome. To reduce the computational load of this alignment, candidate locations can be pre-filtered using simpler alignment algorithms like edit distance. Prior work has explored accelerating filtering on simulated compute-in-DRAM, due to the massive parallelism of compute-in-memory architectures. In this paper, we present work-in-progress on accelerating filtering using a commercial compute-in-SRAM accelerator. We leverage the recently released Gemini accelerator platform from GSI Technology, which is the first, to our knowledge, commercial-scale compute-in-SRAM system. We accelerate the Myers' bit-parallel edit distance algorithm, producing average speedups of 14.1x over single-core CPU performance. Individual query/candidate alignments produce speedups of up to 24.1x. These early results suggest this novel architecture is well-suited to accelerating the filtering step of sequence-to-sequence DNA alignment. |
| title | Accelerating Seed Location Filtering in DNA Read Mapping Using a Commercial Compute-in-SRAM Architecture |
| topic | Hardware Architecture Genomics |
| url | https://arxiv.org/abs/2401.11685 |