Leveraging GPU Acceleration in Bioinformatics for Large-Scale Genomic Data Analysis

EasyChair Preprint 14813

Abstract

The rapid growth of genomic data has created a significant challenge for bioinformatics analysis, necessitating the exploration of innovative computational solutions. This study investigates the potential of GPU acceleration in enhancing large-scale genomic data analysis in bioinformatics. By harnessing the parallel processing capabilities of Graphics Processing Units (GPUs), we demonstrate a substantial acceleration of computational tasks, including sequence alignment, variant calling, and genome assembly. Our results show a significant reduction in processing time, with speedups ranging from 5x to 20x compared to traditional CPU-based approaches. Furthermore, we explore the optimization of existing bioinformatics tools for GPU architectures and develop novel algorithms tailored to leverage GPU acceleration. This research highlights the vast potential of GPU acceleration in bioinformatics, enabling faster and more efficient analysis of large-scale genomic data, and paving the way for new discoveries in the field of genomics and personalized medicine.

Keyphrases: Bioinformatics, GPU acceleration, Genomic Data Analysis

@booklet{EasyChair:14813,
  author    = {Abilly Elly},
  title     = {Leveraging GPU Acceleration in Bioinformatics for Large-Scale Genomic Data Analysis},
  howpublished = {EasyChair Preprint 14813},
  year      = {EasyChair, 2024}}