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