/* This sample demonstrates the way you can perform independed tasks on the different GPUs */ // Disable some warnings which are caused with CUDA headers #if defined(_MSC_VER) #pragma warning(disable: 4201 4408 4100) #endif #include #include "opencv2/core/core.hpp" #include "opencv2/gpu/gpu.hpp" using namespace std; using namespace cv; using namespace cv::gpu; struct Worker: public ParallelLoopBody { virtual void operator() (const Range& range) const { for (int device_id = range.start; device_id != range.end; ++device_id) { setDevice(device_id); Mat src(1000, 1000, CV_32F); Mat dst; RNG rng(0); rng.fill(src, RNG::UNIFORM, 0, 1); // CPU works transpose(src, dst); // GPU works GpuMat d_src(src); GpuMat d_dst; transpose(d_src, d_dst); // Check results bool passed = norm(dst - Mat(d_dst), NORM_INF) < 1e-3; std::cout << "GPU #" << device_id << " (" << DeviceInfo().name() << "): " << (passed ? "passed" : "FAILED") << endl; // Deallocate data here, otherwise deallocation will be performed // after context is extracted from the stack d_src.release(); d_dst.release(); } } }; int main() { int num_devices = getCudaEnabledDeviceCount(); if (num_devices < 2) { std::cout << "Two or more GPUs are required\n"; return -1; } for (int i = 0; i < num_devices; ++i) { cv::gpu::printShortCudaDeviceInfo(i); DeviceInfo dev_info(i); if (!dev_info.isCompatible()) { std::cout << "GPU module isn't built for GPU #" << i << " (" << dev_info.name() << ", CC " << dev_info.majorVersion() << dev_info.minorVersion() << "\n"; return -1; } } // Execute calculation in several threads, 1 GPU per thread parallel_for_(cv::Range(0, num_devices), Worker()); return 0; }