Accelerating Machine Learning Algorithms Using GPU in Bioinformatics Applications

EasyChair Preprint 13741

Abstract

The integration of Graphics Processing Units (GPUs) in bioinformatics has revolutionized the computational landscape, accelerating machine learning algorithms to address complex biological data analysis tasks. This paper explores the impact of GPU acceleration on machine learning algorithms within bioinformatics, highlighting advancements in sequence alignment, genomic data processing, and protein structure prediction. By leveraging the parallel processing capabilities of GPUs, computational efficiency is significantly enhanced, enabling the rapid analysis of vast datasets and facilitating real-time data processing. This acceleration not only reduces computation time but also expands the scope of feasible bioinformatics applications, driving innovation in personalized medicine, disease prediction, and evolutionary studies. The study presents a comparative analysis of GPU-accelerated versus CPU-based implementations, demonstrating substantial performance improvements and discussing the implications for future bioinformatics research and development.

Keyphrases: CPU-based implementations, Graphics Processing Units (GPUs), Machine Learning Algorithms

@booklet{EasyChair:13741,
  author    = {Abill Robert},
  title     = {Accelerating Machine Learning Algorithms Using GPU in Bioinformatics Applications},
  howpublished = {EasyChair Preprint 13741},
  year      = {EasyChair, 2024}}