Accelerating Phylogenetic Tree Construction Using GPU and ML Algorithms

EasyChair Preprint no. 13781

Abstract

Phylogenetic tree construction plays a crucial role in understanding evolutionary relationships among species, genes, or sequences. Traditional methods often face computational challenges due to the vast amount of data and complex algorithms involved. In response, this study explores the acceleration of phylogenetic tree construction using GPU (Graphics Processing Unit) and machine learning (ML) algorithms. GPUs offer parallel processing capabilities ideal for handling the intensive calculations inherent in phylogenetic analysis. ML techniques, particularly deep learning models, are leveraged to optimize tree construction processes by learning from large datasets and improving accuracy and efficiency. This research aims to demonstrate the feasibility and benefits of integrating GPU acceleration with ML algorithms to enhance the speed and accuracy of phylogenetic tree reconstruction, thereby advancing biological research and applications in evolutionary biology, genomics, and biodiversity studies.

Keyphrases: genomics, GPU (Graphics Processing Unit), machine learning

@Booklet{EasyChair:13781,
  author = {Abill Robert},
  title = {Accelerating Phylogenetic Tree Construction Using GPU and ML Algorithms},
  howpublished = {EasyChair Preprint no. 13781},

  year = {EasyChair, 2024}}