How to work with Eigen in CUDA kernels

Question

Eigen is a c++ linear algebra library http://eigen.tuxfamily.org.

It's easy to work with basic data types, like basic float arrays, and just copy it to device memory and pass the pointer to cuda kernels. But Eigen matrix are complex type so how to copy it to device memory and let cuda kernels read/write with it?

Answer 1

Since November 2016 (Release of Eigen 3.3), a new option exists: Using Eigen directly inside CUDA kernels - see this question.

Example from the linked question:

__global__ void cu_dot(Eigen::Vector3f *v1, Eigen::Vector3f *v2, double *out, size_t N)
{
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if(idx < N)
    {
        out[idx] = v1[idx].dot(v2[idx]);
    }
    return;
}

Copying an array of Eigen::Vector3f to device:

Eigen::Vector3f *host_vectors = new Eigen::Vector3f[N];
Eigen::Vector3f *dev_vectors;
cudaMalloc((void **)&dev_vectors, sizeof(Eigen::Vector3f)*N)
cudaMemcpy(dev_vectors, host_vectors, sizeof(Eigen::Vector3f)*N, cudaMemcpyHostToDevice)