High-Performance Bioinformatics: Accelerating Evolutionary Computation with GPUs

EasyChair Preprint 13983

Abstract

The exponential growth of biological data has necessitated the development of advanced computational techniques to efficiently process and analyze complex datasets. Evolutionary computation, inspired by natural selection principles, has emerged as a powerful approach for solving complex optimization problems in bioinformatics. However, the computational demands of evolutionary algorithms often exceed the capabilities of traditional CPU-based systems. This paper explores the transformative potential of Graphics Processing Units (GPUs) in accelerating evolutionary computation for high-performance bioinformatics. By leveraging the parallel processing power of GPUs, we demonstrate significant performance improvements in tasks such as sequence alignment, phylogenetic analysis, and protein structure prediction. Our research showcases how GPU acceleration can drastically reduce computation times, enhance the scalability of evolutionary algorithms, and enable the real-time analysis of large-scale biological datasets. Furthermore, we discuss the integration of GPU-accelerated evolutionary computation into existing bioinformatics workflows, highlighting the practical implications for research and clinical applications. This study underscores the critical role of high-performance computing in advancing bioinformatics and sets the stage for future innovations in computational biology driven by GPU technology.

Keyphrases: Accelerating Evolutionary Computation, Graphics Processing Units, High-Performance Bioinformatics

@booklet{EasyChair:13983,
  author    = {Abi Cit},
  title     = {High-Performance Bioinformatics: Accelerating Evolutionary Computation with GPUs},
  howpublished = {EasyChair Preprint 13983},
  year      = {EasyChair, 2024}}