GPU-Accelerated Big Data Processing for Sustainable Energy and Environmental Solutions

This article explores the application of CUDA-based parallel computing for processing large datasets in sustainability-driven domains, such as environmental monitoring, renewable energy optimization, and smart agriculture. By leveraging the massive parallelism of GPUs, CUDA enables significant acceleration of dataintensive tasks, including real-time climate modeling, energy consumption analysis, and predictive maintenance in green technologies. The article examines CUDA’s architectural advantages, including its ability to process vast amounts of sensor data, satellite imagery, and IoT-generated datasets with high efficiency. Key challenges in CUDA programming – such as memory constraints and thread management – are discussed, along with optimization techniques to enhance performance in sustainability applications. A practical case study demonstrates CUDA’s integration with high-level languages (e.g., C#) for processing large-scale environmental data, showcasing its potential to improve computational efficiency in eco-analytics. The article concludes by highlighting CUDA’s role in advancing big data solutions for a sustainable future, enabling faster decision-making in climate science, smart grids, and ecological resource management.