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https://github.com/jgbit/vuda
VUDA is a header-only library based on Vulkan that provides a CUDA Runtime API interface for writing GPU-accelerated applications.
https://github.com/jgbit/vuda
cuda vuda vulkan
Last synced: 4 days ago
JSON representation
VUDA is a header-only library based on Vulkan that provides a CUDA Runtime API interface for writing GPU-accelerated applications.
- Host: GitHub
- URL: https://github.com/jgbit/vuda
- Owner: jgbit
- License: mit
- Created: 2018-10-06T19:53:52.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-01-21T12:08:08.000Z (11 months ago)
- Last Synced: 2024-11-29T02:15:45.639Z (14 days ago)
- Topics: cuda, vuda, vulkan
- Language: C++
- Homepage:
- Size: 533 KB
- Stars: 867
- Watchers: 35
- Forks: 35
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- AwesomeCppGameDev - vuda - only library based on Vulkan that provides a CUDA Runtime API interface for writing GPU-accelerated applications. (Graphics)
README
## VUDA
VUDA is a header-only library based on Vulkan that provides a CUDA Runtime API interface for writing GPU-accelerated applications.
## Documentation
VUDA is based on the [Vulkan API](https://www.khronos.org/vulkan/). The functionality of VUDA conforms (as much as possible) to the specification of the CUDA runtime. For normal usage consult the reference guide for the [NVIDIA CUDA Runtime API](https://docs.nvidia.com/cuda/cuda-runtime-api/index.html), otherwise check the VUDA wiki:
- [Change List](https://github.com/jgbit/vuda/wiki/Change-List)
- [Setup and Compilation](https://github.com/jgbit/vuda/wiki/Setup-and-Compilation)
- [Deviations from CUDA](https://github.com/jgbit/vuda/wiki/Deviations-from-CUDA)
- [Implementation Details](https://github.com/jgbit/vuda/wiki/Implementation-Details)
## Usage
All VUDA functionality can be accessed by including `vuda.hpp` and using its namespace `vuda::`.
Alternatively, one can utilize `vuda_runtime.hpp` which wraps and redirect all CUDA functionality.
```c++
#if defined(__NVCC__)
#include
#else
#include
#endif
int main(void)
{
// assign a device to the thread
cudaSetDevice(0);
// allocate memory on the device
const int N = 5000;
int a[N], b[N], c[N];
for(int i = 0; i < N; ++i)
{
a[i] = -i;
b[i] = i * i;
}
int *dev_a, *dev_b, *dev_c;
cudaMalloc((void**)&dev_a, N * sizeof(int));
cudaMalloc((void**)&dev_b, N * sizeof(int));
cudaMalloc((void**)&dev_c, N * sizeof(int));
// copy the arrays a and b to the device
cudaMemcpy(dev_a, a, N * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, b, N * sizeof(int), cudaMemcpyHostToDevice);
// run kernel (vulkan shader module)
const int blocks = 128;
const int threads = 128;
#if defined(__NVCC__)
add<<>>(dev_a, dev_b, dev_c, N);
#else
const int stream_id = 0;
vuda::launchKernel("add.spv", "main", stream_id, blocks, threads, dev_a, dev_b, dev_c, N);
#endif
// copy result to host
cudaMemcpy(c, dev_c, N * sizeof(int), cudaMemcpyDeviceToHost);
// do something useful with the result in array c ...
// free memory on device
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_c);
}
```