99 lines
2.3 KiB
C++
99 lines
2.3 KiB
C++
/* This sample demonstrates the way you can perform independed tasks
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on the different GPUs */
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// Disable some warnings which are caused with CUDA headers
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#if defined(_MSC_VER)
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#pragma warning(disable: 4201 4408 4100)
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#endif
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#include <iostream>
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#include "cvconfig.h"
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#include "opencv2/core/core.hpp"
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#include "opencv2/gpu/gpu.hpp"
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#if !defined(HAVE_CUDA) || !defined(HAVE_TBB)
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int main()
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{
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#if !defined(HAVE_CUDA)
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std::cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true).\n";
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#endif
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#if !defined(HAVE_TBB)
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std::cout << "TBB support is required (CMake key 'WITH_TBB' must be true).\n";
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#endif
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return 0;
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}
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#else
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#include "opencv2/core/internal.hpp" // For TBB wrappers
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using namespace std;
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using namespace cv;
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using namespace cv::gpu;
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struct Worker { void operator()(int device_id) const; };
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int main()
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{
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int num_devices = getCudaEnabledDeviceCount();
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if (num_devices < 2)
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{
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std::cout << "Two or more GPUs are required\n";
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return -1;
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}
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for (int i = 0; i < num_devices; ++i)
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{
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cv::gpu::printShortCudaDeviceInfo(i);
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DeviceInfo dev_info(i);
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if (!dev_info.isCompatible())
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{
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std::cout << "GPU module isn't built for GPU #" << i << " ("
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<< dev_info.name() << ", CC " << dev_info.majorVersion()
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<< dev_info.minorVersion() << "\n";
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return -1;
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}
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}
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// Execute calculation in two threads using two GPUs
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int devices[] = {0, 1};
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parallel_do(devices, devices + 2, Worker());
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return 0;
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}
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void Worker::operator()(int device_id) const
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{
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setDevice(device_id);
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Mat src(1000, 1000, CV_32F);
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Mat dst;
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RNG rng(0);
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rng.fill(src, RNG::UNIFORM, 0, 1);
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// CPU works
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transpose(src, dst);
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// GPU works
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GpuMat d_src(src);
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GpuMat d_dst;
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transpose(d_src, d_dst);
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// Check results
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bool passed = norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
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std::cout << "GPU #" << device_id << " (" << DeviceInfo().name() << "): "
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<< (passed ? "passed" : "FAILED") << endl;
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// Deallocate data here, otherwise deallocation will be performed
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// after context is extracted from the stack
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d_src.release();
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d_dst.release();
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}
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#endif
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