Energy Efficient Adaptive Banded Event Alignment using OpenCL on FPGAs

Nanopore sequencing is a third-generation sequencing technology that can read long DNA or RNA fragments in real-time. Nanopore sequencers measure the change in the electrical current when nucleotide bases translocate through a protein nanopore. These signal level changes are utilized in various nanopore data analysis workflows (such as identifying DNA methylation, polishing and variant detection) to obtain useful results from nanopore sequencing data. Adaptive Banded Event Alignment (ABEA) is a dynamic programming algorithm that is used as a key component in many nanopore data analysis workflows. Prior investigations have shown that ABEA consumes 70% of total CPU time in Nanopolish, a popular nanopore data analysis software package. Thus, optimizing the ABEA algorithm is vital for efficient nanopore data analysis. A previous study has proposed an accelerated version of ABEA on GPUs using CUDA that improves the execution time, at the cost of higher energy consumption. With the advancements of High-Level Synthesis (HLS) tools, Field Programmable Gate Arrays (FPGAs) are becoming more and more popular as accelerators that are energy efficient. In this work, we explore the use of the OpenCL for accelerating ABEA on FPGA with energy considerations. We propose a modified version of ABEA for FPGAs using OpenCL and apply various optimization techniques, leading to a few different implementations. We compare the performance of our implementations with other implementations on different hardware platforms in terms of execution time and energy consumption. We show that our best implementation archives an energy consumption of only 43% of the previous implementation of ABEA on GPU, however, with around 4x increase in execution time. We provide all our implementations as open-source software at https://github.com/imsuneth/abea-openclhttps://github.com/imsuneth/abea-opencl