https://github.com/isaurabhmeshram28/cuda-examples
This repository contains examples and experiments with CUDA programming to explore GPU computing and parallel processing using NVIDIA's CUDA framework.
https://github.com/isaurabhmeshram28/cuda-examples
cpp cuda
Last synced: about 1 month ago
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This repository contains examples and experiments with CUDA programming to explore GPU computing and parallel processing using NVIDIA's CUDA framework.
- Host: GitHub
- URL: https://github.com/isaurabhmeshram28/cuda-examples
- Owner: iSaurabhMeshram28
- Created: 2025-04-14T06:47:38.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-01T11:22:55.000Z (about 1 year ago)
- Last Synced: 2025-05-01T12:29:50.290Z (about 1 year ago)
- Topics: cpp, cuda
- Language: C++
- Homepage:
- Size: 242 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Cuda-Example
This repository contains examples and experiments with CUDA programming to explore GPU computing and parallel processing using NVIDIA's CUDA framework. It demonstrates how to leverage the power of GPUs for high-performance computing tasks, written in C++ and CUDA.
## Repository Overview
- To learn and demonstrate the fundamentals of CUDA programming and its applications in parallel computing.
## Contents
The repository is organized into examples and experiments covering:
1. **CUDA Basics**: Introduction to CUDA syntax, kernels, and memory management.
2. **Parallel Processing**: Examples that demonstrate how to perform parallel computations using CUDA.
## Prerequisites
To run the examples in this repository, you will need:
- NVIDIA GPU with CUDA support
- NVIDIA CUDA Toolkit installed
- A modern C++ compiler (e.g., GCC or MSVC)
## Getting Started
1. Clone the repository:
```bash
git clone https://github.com/iSaurabhMeshram28/Cuda-Example.git
```
2. Navigate to the project directory:
```bash
cd Cuda-Example
```
3. Compile and run an example:
```bash
nvcc example.cu -o example
./example
```
## Learning Objectives
This repository is designed to help users:
- Understand the basics of CUDA programming.
- Write and optimize parallel computing code.
- Explore the potential of GPU acceleration for computational tasks.
## Future Work
- Add more complex examples, such as matrix multiplication and image processing.
- Explore interoperability between CUDA and other frameworks like OpenGL or OpenCV.
- Optimize existing examples for better performance.
## Resources
- Astromedicomp's HPP Seminar
- [CUDA Documentation](https://docs.nvidia.com/cuda/)
- [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit)
- [NVIDIA Developer Blog](https://developer.nvidia.com/blog)