FPGA Acceleration of Pre-Alignment Filters for Short Read Mapping With HLS

Abstract

Pre-alignment filters are useful for reducing the computational requirements of genomic sequence mappers. Most of them are based on estimating or computing the edit distance between sequences and their candidate locations in a reference genome using a subset of the dynamic programming table used to compute Levenshtein distance. Some of their FPGA implementations of use classic HDL toolchains, thus limiting their portability. Currently, most FPGA accelerators offered by heterogeneous cloud providers support C/C++ HLS. In this work, we reimplement and optimize several state-of-the-art pre-alignment filters using C/C++ based HLS to expand their portability to a wide range of systems supporting the OpenCL runtime. Moreover, we perform a complete analysis of the performance and accuracy of the filters and analyze the implications of the results. The maximum throughput obtained by an exact filter is 95.1 MPairs/s including memory transfers using 100 bp sequences, which is the highest ever reported for a comparable system and more than two times faster than previous HDL-based results. The best energy efficiency obtained from the accelerator (not considering host CPU) is 2.1 MPairs/J, more than one order of magnitude higher than other accelerator-based comparable approaches from the state of the art.

Integrating FPGA Acceleration in the DNAssim Framework for Faster DNA-Based Data Storage Simulations

“…Castells-Rufas et al [32] developed an approximate edit distance accelerator validated on several up-to-date FPGA architectures (with PCIe gen3 ×16 host interface) reporting topclass performance in the context of DNA string alignment, although not providing a direct link with the clustering algorithm nor with DNA-based storage simulation acceleration.…”