High-Performance Computing for Comparative Genomics Using GPU and ML

EasyChair Preprint no. 13999

Abstract

In the era of genomics, the ability to analyze and compare vast amounts of genetic data efficiently is critical for advancing our understanding of evolutionary biology, disease mechanisms, and species diversity. Traditional computational methods often fall short in handling the scale and complexity of modern genomic datasets. This paper explores the integration of High-Performance Computing (HPC) with Graphics Processing Units (GPUs) and Machine Learning (ML) techniques to enhance comparative genomics. By leveraging the parallel processing power of GPUs, we can significantly accelerate computational tasks such as sequence alignment, phylogenetic tree construction, and genomic variation analysis. Additionally, ML algorithms are employed to predict functional annotations and evolutionary relationships with greater accuracy and speed. Our findings demonstrate that this hybrid approach not only reduces computational time but also improves the precision of comparative genomics analyses. We present case studies that highlight the application of GPU-accelerated ML models in identifying conserved genetic elements across different species and uncovering insights into genomic adaptations. The results underscore the potential of HPC, GPUs, and ML to transform comparative genomics, making it more accessible and efficient for researchers worldwide.

Keyphrases: Graphics Processing Units (GPUs), High Performance Computing, Machine Learning (ML)

@Booklet{EasyChair:13999,
  author = {Abi Cit},
  title = {High-Performance Computing for Comparative Genomics Using GPU and ML},
  howpublished = {EasyChair Preprint no. 13999},

  year = {EasyChair, 2024}}