Accessing cusp variable element from device kernel

Question

I have a problem to access and assign variable with cusp array1d type from device/global kernel. The attached code gives error

alay.cu(8): warning: address of a host variable "p1" cannot be directly taken in a device function

alay.cu(8): error: calling a __host__ function("thrust::detail::vector_base<float, thrust::device_malloc_allocator<float> > ::operator []") from a __global__ function("func") is not allowed

Code Below

#include <cusp/blas.h>
cusp::array1d<float, cusp::device_memory> p1(10,3);
__global__ void func()
{
p1[blockIdx.x]=p1[blockIdx.x]+blockIdx.x*5;
}
int main()
{
func<<<10,1>>>();
return 0;
}

Answer 1

CUSP matrices and arrays (and the Thrust containers they are built with) are intended for host use only. You cannot directly use them in GPU code.

The canonical way to populate a CUSP sparse matrix would be to construct it in host memory and the copy it across to device memory using the copy constructor, so your trivial example becomes this:

cusp::array1d<float, cusp::host_memory> p1(10);
for(int i=0; i<10; i++) p1[i] = 4.f;
cusp::array1d<float, cusp::device_memory> p2(10) = p1; // data now on device

If you want to manipulate a sparse matrix in device code, you will need to have a kernel specifically for whichever format you are interested in, and pass pointers to each of the device arrays holding the matrix data as arguments to that kernel. There is good Doxygen source annotation for all of the sparse types included in the CUSP distribution.

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Your edit still doesn't present anything which couldn't be done on the host without a kernel, viz:

cusp::array1d<float, cusp::host_memory> p1(10, 3.f);
for(int i=0; i<10; i++) p1[i] += (i * 5.f);
cusp::array1d<float, cusp::device_memory> p2(10) = p1; // data now on device