I am using OpenCL (Xcode, Intel GPU), and I am trying to implement a kernel that calculates moving averages and deviations. I want to pass several double arrays of varying lengths to the kernel. Is this possible to implement, or do I need to pad smaller arrays with zeroes so all the arrays are the same size?
I am new to OpenCL and GPGPU, so please forgive my ignorance of any nomenclature.
You can pass to the kernel any buffer, the kernel does not need to use it all.
For example, if your kernel reduces a buffer you can query at run time the amount of work items (items to reduce) using get_global_size(0). And then call the kernel with the proper parameters.
An example (unoptimized):
__kernel reduce_step(__global float* data)
{
int id = get_global_id(0);
int size = get_global_size(0);
int size2 = size/2;
int size2p = (size+1)/2;
if(id<size2) //Only reduce up to size2, the odd element will remain in place
data[id] += data[id+size2p];
}
Then you can call it like this.
void reduce_me(std::vector<cl_float>& data){
size_t size = data.size();
//Copy to a buffer already created, equal or bigger size than data size
// ... TODO, check sizes of buffer or change the buffer set to the kernel args.
queue.enqueueWriteBuffer(buffer,CL_FALSE,0,sizeof(cl_float)*size,data.data());
//Reduce until 1024
while(size > 1024){
queue.enqueueNDRangeKernel(reduce_kernel,cl::NullRange,cl::NDRange(size),cl::NullRange);
size /= 2;
}
//Read out and trim
queue.enqueueReadBuffer(buffer,CL_TRUE,0,sizeof(cl_float)*size,data.data());
data.resize(size);
}
get_global_size(n) get element n from the globalWorkSize [ ]? And how do you use the get_global_size() function to accomplish different things? Any tutorials or examples you know that would help.My ideal situation would be to pass a large constant of arrays, each containing variable length arrays. I had the idea to standardize the arrays' length by padding with zeroes and fold it into one extremely large array, then use modulus operator in kernel to derive where arrays change. But this seems expensive to memory? Thanks