https://github.com/isquicha/cuda-parallel-studies
Learning CUDA programming here =D
https://github.com/isquicha/cuda-parallel-studies
cuda cuda-programming cuda-toolkit
Last synced: 12 months ago
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Learning CUDA programming here =D
- Host: GitHub
- URL: https://github.com/isquicha/cuda-parallel-studies
- Owner: isquicha
- Created: 2020-11-05T07:10:10.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2020-11-05T13:03:48.000Z (over 5 years ago)
- Last Synced: 2025-06-04T00:33:45.160Z (about 1 year ago)
- Topics: cuda, cuda-programming, cuda-toolkit
- Language: Cuda
- Homepage:
- Size: 3.91 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Cuda Parallel Programming Studies
## Description
My GPU was expensive, so I have to use it.
As I can't be angry only with games, I'm becoming upset with C++ and memory allocation again, after some good months with Python.
Learning CUDA programming here =D
## Information
The .cu files are basically .cpp with some CUDA syntax.
In CUDA context, Host = CPU and Device = GPU.
Variables with a `h` in the name are host's variables.
The ones with an `d` are device's variables.
The CUDA headers (.h files) are on `CUDA Toolkit installation folder/include`.
## How to run
### Requirements
- A NVIDIA GPU (I have a GTX 1050 Ti)
- Install CUDA Toolkit (remember to read the toolkit installation requirements
- [Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html)
- [Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
### Steps to run
- Download the .cu file or `git clone` this repository
- Compile your `.cu` with `nvcc`
- `nvcc myfile.cu`
- Run the generated file
- Windows: `a.exe`
- Linux (not tested): `./a.out`
## Content
| Project | Description |
| :----------------: | :---------------------------------------------------------------------: |
| [00](src/00/00.cu) | Basic vector adder, with CPU vs GPU comparison (a kind of hello world). |
| 01 | To implement |