GPU-Enhanced Computational Biology: Accelerating Simulation of Biological Systems

EasyChair Preprint no. 13816

Abstract

computational biology, particularly in accelerating the simulation of complex biological systems. This paper explores the transformative impact of GPU-enhanced computing on the field of computational biology, focusing on its ability to significantly reduce simulation times and enhance the accuracy of models. By harnessing the parallel processing capabilities of GPUs, researchers can tackle larger datasets and more intricate biological phenomena with unprecedented efficiency. This abstract discusses key methodologies and advancements in GPU-accelerated simulations, highlighting their implications for understanding biological processes at various scales, from molecular dynamics to ecological systems. The adoption of GPU technology promises to reshape the landscape of computational biology, offering new avenues for exploring biological complexity and advancing scientific discovery.

The rapid advancements in computational power have significantly transformed numerous scientific fields, with computational biology being one of the most profoundly impacted. Traditional central processing units (CPUs), while effective for a range of tasks, often struggle with the immense computational demands of simulating complex biological systems. Enter Graphics Processing Units (GPUs), originally designed to handle the massive parallel processing requirements of graphics rendering. These versatile processors have found a new application in computational biology, offering a powerful solution to the computational bottlenecks faced by researchers.

Keyphrases: Accelerated sequence analysis, Bioinformatic algorithms, Computational genomics, Computational Proteomics, Deep learning in bioinformatics, Genomic data processing, GPU-accelerated machine learning, GPU-based bioinformatics, High Performance Computing, Machine learning in computational biology

@Booklet{EasyChair:13816,
  author = {Abill Robert},
  title = {GPU-Enhanced Computational Biology: Accelerating Simulation of Biological Systems},
  howpublished = {EasyChair Preprint no. 13816},

  year = {EasyChair, 2024}}