VUDA is a header-only library based on Vulkan that provides a CUDA Runtime API interface for writing GPU-accelerated applications.
VUDA is based on the Vulkan API. 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, otherwise check the VUDA wiki:
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.
#if defined(__NVCC__)
#include <cuda_runtime.h>
#else
#include <vuda_runtime.hpp>
#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<<<blocks, threads>>>(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);
}