int main(int argc, char **argv) {
if(argc > 2) {
simple_add(argv[1], atoi(argv[2]));
}
}
int main(void) {
simple_add();
simple_added();
simple_adding();
}
int main() {
// get all platforms (drivers), e.g. NVIDIA
std::vector<cl::Platform> all_platforms;
cl::Platform::get(&all_platforms);
if (all_platforms.size()==0) {
std::cout<<" No platforms found. Check OpenCL installation!\n";
exit(1);
}
cl::Platform default_platform=all_platforms[0];
std::cout << "Using platform: "<<default_platform.getInfo<CL_PLATFORM_NAME>()<<"\n";
// get default device (CPUs, GPUs) of the default platform
std::vector<cl::Device> all_devices;
default_platform.getDevices(CL_DEVICE_TYPE_ALL, &all_devices);
if(all_devices.size()==0){
std::cout<<" No devices found. Check OpenCL installation!\n";
exit(1);
}
// use device[1] because that's a GPU; device[0] is the CPU
cl::Device default_device=all_devices[1];
std::cout<< "Using device: "<<default_device.getInfo<CL_DEVICE_NAME>()<<"\n";
// a context is like a "runtime link" to the device and platform;
// i.e. communication is possible
cl::Context context({default_device});
// create the program that we want to execute on the device
cl::Program::Sources sources;
// calculates for each element; C = A + B
std::string kernel_code=
" void kernel simple_add(global const int* A, global const int* B, global int* C, "
" global const int* N) {"
" int ID, Nthreads, n, ratio, start, stop;"
""
" ID = get_global_id(0);"
" Nthreads = get_global_size(0);"
" n = N[0];"
""
" ratio = (n / Nthreads);" // number of elements for each thread
" start = ratio * ID;"
" stop = ratio * (ID + 1);"
""
" for (int i=start; i<stop; i++)"
" C[i] = A[i] + B[i];"
" }";
sources.push_back({kernel_code.c_str(), kernel_code.length()});
cl::Program program(context, sources);
if (program.build({default_device}) != CL_SUCCESS) {
std::cout << "Error building: " << program.getBuildInfo<CL_PROGRAM_BUILD_LOG>(default_device) << std::endl;
exit(1);
}
// apparently OpenCL only likes arrays ...
// N holds the number of elements in the vectors we want to add
int N[1] = {100};
int n = N[0];
// create buffers on device (allocate space on GPU)
cl::Buffer buffer_A(context, CL_MEM_READ_WRITE, sizeof(int) * n);
cl::Buffer buffer_B(context, CL_MEM_READ_WRITE, sizeof(int) * n);
cl::Buffer buffer_C(context, CL_MEM_READ_WRITE, sizeof(int) * n);
cl::Buffer buffer_N(context, CL_MEM_READ_ONLY, sizeof(int));
// create things on here (CPU)
int A[n], B[n];
for (int i=0; i<n; i++) {
A[i] = i;
B[i] = n - i - 1;
}
// create a queue (a queue of commands that the GPU will execute)
cl::CommandQueue queue(context, default_device);
// push write commands to queue
queue.enqueueWriteBuffer(buffer_A, CL_TRUE, 0, sizeof(int)*n, A);
queue.enqueueWriteBuffer(buffer_B, CL_TRUE, 0, sizeof(int)*n, B);
queue.enqueueWriteBuffer(buffer_N, CL_TRUE, 0, sizeof(int), N);
// RUN ZE KERNEL
cl::KernelFunctor simple_add(cl::Kernel(program, "simple_add"), queue, cl::NullRange, cl::NDRange(10), cl::NullRange);
simple_add(buffer_A, buffer_B, buffer_C, buffer_N);
int C[n];
// read result from GPU to here
queue.enqueueReadBuffer(buffer_C, CL_TRUE, 0, sizeof(int)*n, C);
std::cout << "result: {";
for (int i=0; i<n; i++) {
std::cout << C[i] << " ";
}
std::cout << "}" << std::endl;
return 0;
}
