Hello everyone,
A little question here.
Making a code in CUDA, I needed a variable shared memory size. I believed I couldn’t do that, but the function cudaConfigureCall then dazzled me.
I don’t quite know how to work with that function, specifically how to setup the shared memory size.
So my question is, can I use cudaConfigureCall to call a kernel with a variable-depending shared memory size, instead of a constant shared memory size?
Just use the third parameter to the kernel invocation:
kernel<<<grid, threads, num_extern_shared_bytes>>>(args)
Thanks, MisterAnderson, but how does that work in the called function?
For example, how would I change this function to use that parameter in the variable “M”:
__global__ static void kernel(args){
__shared__ short M[MAX_SIZE];
...
}
Do this:
__global__ static void kernel(args){
extern __shared__ short shmem[];
shmem[0] = ...;
...
}
If you need to know the size of the shared memory in the kernel you can pass it in as an argument, but in many cases the size you need is a natural consequence of the number of threads in a block, so you don’t need to pass it in.