CUDA Calculation of Shannon Entropy for a Sliding Window System

Abstract

Entropy algorithms are crucial in fields where assessing randomness, uncertainty, or complexity is vital. As datasets grow, efficient entropy calculations become important. This work explores the parallelization of Shannon entropy calculations, using GPU acceleration through CUDA for sliding window systems. By leveraging GPUs’ parallel architecture, the approach achieves up to 15x speedup for large datasets. However, smaller datasets show limited improvements due to overhead, underscoring the need for optimization to harness GPU acceleration’s potential.