GPU-Enhanced Visualization of Large-Scale Bioinformatics Data

EasyChair Preprint 13992

Abstract

The rapid advancements in bioinformatics have led to an exponential increase in the generation of large-scale biological data, necessitating efficient methods for data analysis and visualization. This paper explores the transformative potential of Graphics Processing Units (GPUs) in enhancing the visualization of vast and complex bioinformatics datasets. GPUs, with their parallel processing capabilities, offer significant advantages over traditional Central Processing Units (CPUs) by accelerating data-intensive computations and enabling real-time rendering of intricate biological structures. We present a comprehensive review of current GPU-accelerated visualization tools and techniques, highlighting their applications in genomics, proteomics, and metagenomics. Case studies demonstrate how GPU-enhanced visualization can improve the interpretation of multi-dimensional datasets, facilitating more accurate and timely insights into biological processes and disease mechanisms. By leveraging GPU technology, researchers can overcome the limitations of conventional visualization methods, leading to more robust and scalable bioinformatics solutions. This paper underscores the importance of integrating GPU-based approaches in bioinformatics workflows to drive innovation and advance our understanding of complex biological systems.

Keyphrases: Bioinformatics Data, Central Processing Units, Graphics Processing Units

@booklet{EasyChair:13992,
  author    = {Abi Cit},
  title     = {GPU-Enhanced Visualization of Large-Scale Bioinformatics Data},
  howpublished = {EasyChair Preprint 13992},
  year      = {EasyChair, 2024}}