device_matrix | device_matrix library is a lightweight transparent | GPU library

There are a number of issues:

• You are creating a 1-D grid (grid configuration, block configuration) so your 2-D indexing in kernel code (i,j, or x,y) doesn't make any sense
• You are passing sum by value. You cannot retrieve a result that way. Changes in the kernel to sum won't be reflected in the calling environment. This is a C++ concept, not specific to CUDA. Use a properly allocated pointer instead.
• In a CUDA multithreading environment, you cannot have multiple threads update the same location/value without any control. CUDA does not sort out that kind of access for you. You must use a parallel reduction technique, and a simplistic approach here could be to use atomics. You can find many questions here on the cuda tag discussing parallel reductions.
• You're generally confusing pass by value and pass by pointer. Items passed by value can be ordinary host variables. You generally don't need a cudaMalloc allocation for those. You also don't use cudaMalloc on any kind of variable except a pointer.
• Your use of cudaMemcpy is incorrect. There is no need to take the address of the pointers.

The following code has the above items addressed: