Up until a few years ago, the following demonstrative CUDA code was perfectly workable:

Up until a few years ago, the following demonstrative CUDA code was perfectly workable:

I currently have a C/CUDA program which uses multiple CPUs to generate values in parallel to be passed to kernel functions which run on a GPU.

I have a nested data structure which is stored on both host and device. I would like to copy the relevant inner field from host to device. Assume I have done all the allocations correct. Then, I would need the address of the innermost member on the device side (which I am obtaining via a kernel launch) and then storing this address into a dummy variable (which I am doing via cudaGetSymbolAddress) and then performing the copy (through cudaMemcpy). However, it doesn’t seem to work. The following is a snippet of the code:

I’m programming a tree structure in CUDA. I have the GPU copy all of the data in the leaves to an output array and then print the output array. This works perfectly fine, except I want to be able to modify my tree during runtime.

So I am quite new to the Parallel programming world. One thing I can not wrap my head around is the concept of threads, thread blocks and grid blocks.