Background Adaptive Banded Event Alignment (ABEA) stands as a critical algorithmic component in sequence polishing and DNA methylation detection, employing dynamic programming to align raw Nanopore signal with reference reads. Motivated by the observation that, compared to CPUs and GPUs, cutting-edge FPGAs demonstrate—in certain cases—superior performance at a reduced cost and energy consumption, this paper presents an efficient FPGA-based accelerator for ABEA, leveraging the inherent high parallelism and sequential access pattern within ABEA. Result Our proposed FPGA-based ABEA accelerator significantly enhances ABEA performance compared to the original CPU-based implementation in Nanopolish as well as the state-of-art acceleration on GPU and FPGA platforms. Specifically, targeting Xilinx VU9P, our accelerator achieves an average throughput speedup of 10.05<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12859_2024_6011_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$\times$</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> over the CPU-only implementation, an average 1.81<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12859_2024_6011_Article_IEq2.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$\times$</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> speedup over the state-of-art GPU acceleration with only 7.2% of the energy, and a speedup of 10.11<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12859_2024_6011_Article_IEq3.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$\times$</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> compared to an existing FPGA accelerator. Conclusion Our work demonstrates that intensive genome analysis can benefit significantly from cutting-edge FPGAs, offering improvements in both performance and energy consumption.

FPGA-based accelerator for adaptive banded event alignment in nanopore sequencing data analysis

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2025年3月17日

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Yilin Feng,
Zheyu Li,
Gulsum Gudukbay Akbulut,
Vijaykrishnan Narayanan,
Mahmut Taylan Kandemir,
Chita R. Das

摘要

Background

Adaptive Banded Event Alignment (ABEA) stands as a critical algorithmic component in sequence polishing and DNA methylation detection, employing dynamic programming to align raw Nanopore signal with reference reads. Motivated by the observation that, compared to CPUs and GPUs, cutting-edge FPGAs demonstrate—in certain cases—superior performance at a reduced cost and energy consumption, this paper presents an efficient FPGA-based accelerator for ABEA, leveraging the inherent high parallelism and sequential access pattern within ABEA.

Result

Our proposed FPGA-based ABEA accelerator significantly enhances ABEA performance compared to the original CPU-based implementation in Nanopolish as well as the state-of-art acceleration on GPU and FPGA platforms. Specifically, targeting Xilinx VU9P, our accelerator achieves an average throughput speedup of 10.05 $\times$ $\times$ over the CPU-only implementation, an average 1.81 $\times$ $\times$ speedup over the state-of-art GPU acceleration with only 7.2% of the energy, and a speedup of 10.11 $\times$ $\times$ compared to an existing FPGA accelerator.

Conclusion

Our work demonstrates that intensive genome analysis can benefit significantly from cutting-edge FPGAs, offering improvements in both performance and energy consumption.