Bioinformatics workloads are characterized by huge data sets and complex algorithms, requiring enormous data processing and making high performance heterogeneous computation platforms such as FPGAs and GPUs highly relevant. We compare three accelerated implementations of the widely used BWA-MEM genomic mapping tool as a case study on design-time optimization for heterogeneous architectures: BWA-MEM-CUDA, BWA-MEM-OpenCL, and BWA-MEMVHDL, each using an optimized Smith-Waterman algorithm implementation. Optimization of design-time is important because of the significant development effort of such implementations: BWA-MEM-CUDA and BWA-MEM-OpenCL require 5-7x more lines of code to express the Smith-Waterman algorithm, while BWA-MEM-VHDL requires more than 40x as many lines of code. Similar differences hold for required implementation time, ranging from one month for BWA-MEMOpenCL to six months for BWA-MEM-VHDL. The advantages and disadvantages of each implementation are described using both quantitative and qualitative metrics, and recommendations are given for future algorithm implementations.
Original languageEnglish
Title of host publication2018 IEEE 18th International Conference on BioInformatics and BioEngineering (BIBE)
EditorsNikolaos G. Bourbakis, Despina Kavraki
Place of PublicationPiscataway, NJ. USA
Number of pages4
ISBN (Electronic)978-1-5386-6217-5
ISBN (Print)978-1-5386-5043-1
Publication statusPublished - 2018
Event18th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2018 - Taiching, Taiwan, Province of China
Duration: 29 Oct 201831 Oct 2018


Conference18th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2018
CountryTaiwan, Province of China

    Research areas

  • Graphics processing units, Field programmable gate arrays, Kernel, Acceleration, Hardware, instruction sets, Bioinformatics

ID: 47895522