This project explores the difference in computing power between a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit). It demonstrates how parallel computing on a GPU can significantly outperform the serial computing of a CPU, using image processing tasks as a benchmark.
Grayscale conversion and Gaussian blur (which involves convolution to smooth the image.) Both tasks are executed using a CPU and a GPU. The time taken for each computation is measured to provide a clear comparison of their performance.
CUDA Optimization: CUDA (Compute Unified Device Architecture) is used to leverage the GPU's parallel processing capabilities, making it possible to compute multiple pixels simultaneously.
