Project Structure Introduction to GPU Computing and CUDA
Overview of GPU architecture and CUDA. Importance of parallelization and its impact on performance. Brief introduction to Python libraries for GPU computing (e.g., CuPy). Example Implementations
Matrix Multiplication: Sequential (CPU): Implement matrix multiplication using NumPy. Parallel (GPU): Use CuPy for parallel matrix multiplication. Image Processing: Filtering (e.g., Gaussian Blur): Sequential (CPU): Implement using OpenCV. Parallel (GPU): Use CuPy for the same filters. Edge Detection (e.g., Sobel Filter): Sequential (CPU): Implement using OpenCV. Parallel (GPU): Use CuPy for the edge detection filters. Machine Learning: Neural Network for Classification: Sequential (CPU): Implement a neural network from scratch using NumPy. Parallel (GPU): Implement the same neural network using CuPy for GPU acceleration. Image Segmentation using K-means Clustering: Sequential (CPU): Implement the K-means algorithm for image segmentation. Parallel (GPU): Optimize the K-means algorithm using CuPy.