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arXiv · 2203.15561

Algorithmic Improvement and GPU Acceleration of the GenASM Algorithm

Joël Lindegger·Damla Senol Cali·Mohammed Alser·Juan Gómez-Luna·Onur Mutlu
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Abstract

We improve on GenASM, a recent algorithm for genomic sequence alignment, by significantly reducing its memory footprint and bandwidth requirement. Our algorithmic improvements reduce the memory footprint by 24$\times$ and the number of memory accesses by 12$\times$. We efficiently parallelize the algorithm for GPUs, achieving a 4.1$\times$ speedup over a CPU implementation of the same algorithm, a 62$\times$ speedup over minimap2's CPU-based KSW2 and a 7.2$\times$ speedup over the CPU-based Edlib for long reads.

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Joël Lindegger, Damla Senol Cali, Mohammed Alser, Juan Gómez-Luna, Onur Mutlu. 2022-03-28. Algorithmic Improvement and GPU Acceleration of the GenASM Algorithm. https://arxiv.org/abs/2203.15561

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