Real-Time Evolutionary Dynamics Simulation Using GPU and ML

EasyChair Preprint 14131

Abstract

Real-time evolutionary dynamics simulation has emerged as a pivotal tool for understanding and predicting the complex processes governing biological evolution. Traditional computational methods, however, often fall short in handling the immense data and intricate calculations required for accurate simulations. Leveraging the power of Graphics Processing Units (GPUs) and Machine Learning (ML) presents a transformative approach to overcome these limitations. This paper explores the integration of GPU-accelerated computing and advanced ML algorithms to enhance the efficiency and accuracy of evolutionary dynamics simulations. By utilizing parallel processing capabilities of GPUs, we achieve significant reductions in computation time, enabling real-time analysis and visualization of evolutionary processes. Furthermore, ML algorithms facilitate adaptive modeling and predictive analytics, allowing for more refined and responsive simulations. Case studies highlight the application of this integrated approach in various evolutionary scenarios, demonstrating its potential to revolutionize research in evolutionary biology. The findings underscore the importance of GPU and ML in advancing real-time evolutionary dynamics simulations, providing a robust framework for future studies and applications in the field.

Keyphrases: CUDA (Compute Unified Device Architecture), Graphics Processing Units (GPUs), Machine Learning (ML)

@booklet{EasyChair:14131,
  author    = {Abi Litty},
  title     = {Real-Time Evolutionary Dynamics Simulation Using GPU and ML},
  howpublished = {EasyChair Preprint 14131},
  year      = {EasyChair, 2024}}