GPU-Accelerated Predictive Modeling for Microbial Genomics

EasyChair Preprint 13988

Abstract

Microbial genomics, the study of microbial DNA sequences, holds immense potential for advancing our understanding of microbial functions and interactions in various environments. Predictive modeling in this field is essential for applications ranging from healthcare to agriculture and environmental management. However, the sheer volume and complexity of genomic data present significant computational challenges. This paper explores the use of Graphics Processing Units (GPUs) to accelerate predictive modeling in microbial genomics, offering substantial performance improvements over traditional CPU-based methods. By leveraging the parallel processing capabilities of GPUs, we demonstrate enhanced efficiency in tasks such as genome assembly, sequence alignment, and variant calling. We also explore the application of GPU-accelerated machine learning algorithms for predicting microbial behavior and interactions, enabling faster and more accurate insights. Our findings indicate that GPU acceleration can significantly reduce computational time, making it feasible to handle large-scale genomic datasets and complex predictive models. This advancement not only enhances the speed and accuracy of microbial genomic analyses but also opens new avenues for real-time applications in clinical diagnostics, bioengineering, and environmental monitoring.

Keyphrases: Central Processing Units, Graphics Processing Units, Microbial genomics

@booklet{EasyChair:13988,
  author    = {Abi Cit},
  title     = {GPU-Accelerated Predictive Modeling for Microbial Genomics},
  howpublished = {EasyChair Preprint 13988},
  year      = {EasyChair, 2024}}