Code optimization of multiple sequence alignment software tool MSA_BG on GPU-accelerated computing infrastructures

Multiple biological sequences alignment is one of the fundamental tasks in computational biology. The problem is NP-hard and has been subjected to intense research during the last 10 years. Exact MSA algorithms such as Clustawl have considerable serial sections (for building up the guide tree) which limit the efficiency of code parallelization and optimization. In the era of Big data, the Big genomic data ecosystem has accumulated huge amounts of genomic data provoking the challenge for innovative massively parallel algorithmic paradigms targeted for the efficient exploitation of the abandon parallel hardware resources within the high performance computing infrastructure. The goal of our investigation is to design and implement software tool for massively parallel multiple sequence alignment based on our randomized method for massively parallel multiple sequence alignment targeted for GPU accelerated computing infrastructures. Parallel performance evaluation analysis shows efficient scalability in respect to data size and machine size.Multiple biological sequences alignment is one of the fundamental tasks in computational biology. The problem is NP-hard and has been subjected to intense research during the last 10 years. Exact MSA algorithms such as Clustawl have considerable serial sections (for building up the guide tree) which limit the efficiency of code parallelization and optimization. In the era of Big data, the Big genomic data ecosystem has accumulated huge amounts of genomic data provoking the challenge for innovative massively parallel algorithmic paradigms targeted for the efficient exploitation of the abandon parallel hardware resources within the high performance computing infrastructure. The goal of our investigation is to design and implement software tool for massively parallel multiple sequence alignment based on our randomized method for massively parallel multiple sequence alignment targeted for GPU accelerated computing infrastructures. Parallel performance evaluation analysis shows efficient scalability in respect...