added stereo_multi_gpu sample, cosmetic changes in multi_gpu sample
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ed77955635
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ea94b43541
@ -357,7 +357,7 @@ extern "C" void stereoBM_GPU(const DevMem2D& left, const DevMem2D& right, const
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cudaSafeCall( cudaMemset2D(disp.data, disp.step, 0, disp.cols, disp.rows) );
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cudaSafeCall( cudaMemset2D(disp.data, disp.step, 0, disp.cols, disp.rows) );
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cudaSafeCall( cudaMemset2D(minSSD_buf.data, minSSD_buf.step, 0xFF, minSSD_buf.cols * minSSD_buf.elemSize(), disp.rows) );
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cudaSafeCall( cudaMemset2D(minSSD_buf.data, minSSD_buf.step, 0xFF, minSSD_buf.cols * minSSD_buf.elemSize(), disp.rows) );
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cudaSafeCall( cudaMemcpyToSymbol( cwidth, &left.cols, sizeof(left.cols) ) );
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cudaSafeCall( cudaMemcpyToSymbol( cwidth, &left.cols, sizeof(left.cols) ) );
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cudaSafeCall( cudaMemcpyToSymbol( cheight, &left.rows, sizeof(left.rows) ) );
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cudaSafeCall( cudaMemcpyToSymbol( cheight, &left.rows, sizeof(left.rows) ) );
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cudaSafeCall( cudaMemcpyToSymbol( cminSSDImage, &minSSD_buf.data, sizeof(minSSD_buf.data) ) );
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cudaSafeCall( cudaMemcpyToSymbol( cminSSDImage, &minSSD_buf.data, sizeof(minSSD_buf.data) ) );
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@ -1,3 +1,6 @@
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/* 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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// Disable some warnings which are caused with CUDA headers
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#pragma warning(disable: 4201 4408 4100)
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#pragma warning(disable: 4201 4408 4100)
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@ -34,41 +37,50 @@ using namespace cv::gpu;
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struct Worker { void operator()(int device_id) const; };
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struct Worker { void operator()(int device_id) const; };
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void destroyContexts();
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void destroyContexts();
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#define cuSafeCall(code) if (code != CUDA_SUCCESS) { \
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#define safeCall(code) if (code != CUDA_SUCCESS) { \
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cout << "CUDA driver API error: code " << code \
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cout << "CUDA driver API error: code " << code \
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<< ", file " << __FILE__ << ", line " << __LINE__ << endl; \
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<< ", file " << __FILE__ << ", line " << __LINE__ << endl; \
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destroyContexts(); \
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destroyContexts(); \
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exit(-1); \
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exit(-1); \
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}
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}
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// Each GPU is associated with its own context
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// Each GPU is associated with its own context
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CUcontext contexts[2];
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CUcontext contexts[2];
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int main()
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int main()
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{
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{
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if (getCudaEnabledDeviceCount() < 2)
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int num_devices = getCudaEnabledDeviceCount();
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if (num_devices < 2)
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{
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{
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cout << "Two or more GPUs are required\n";
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cout << "Two or more GPUs are required\n";
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return -1;
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return -1;
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}
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}
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cuSafeCall(cuInit(0));
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for (int i = 0; i < num_devices; ++i)
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{
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if (!DeviceInfo(i).isCompatible())
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{
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cout << "GPU module isn't built for GPU #" << i << " (" << DeviceInfo(i).name() << ")";
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return -1;
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}
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}
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// Create context for the first GPU
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safeCall(cuInit(0));
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// Create context for GPU #0
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CUdevice device;
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CUdevice device;
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cuSafeCall(cuDeviceGet(&device, 0));
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safeCall(cuDeviceGet(&device, 0));
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cuSafeCall(cuCtxCreate(&contexts[0], 0, device));
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safeCall(cuCtxCreate(&contexts[0], 0, device));
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CUcontext prev_context;
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CUcontext prev_context;
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cuSafeCall(cuCtxPopCurrent(&prev_context));
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safeCall(cuCtxPopCurrent(&prev_context));
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// Create context for the second GPU
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// Create context for GPU #1
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cuSafeCall(cuDeviceGet(&device, 1));
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safeCall(cuDeviceGet(&device, 1));
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cuSafeCall(cuCtxCreate(&contexts[1], 0, device));
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safeCall(cuCtxCreate(&contexts[1], 0, device));
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cuSafeCall(cuCtxPopCurrent(&prev_context));
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safeCall(cuCtxPopCurrent(&prev_context));
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// Execute calculation in two threads using two GPUs
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// Execute calculation in two threads using two GPUs
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int devices[] = {0, 1};
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int devices[] = {0, 1};
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@ -81,8 +93,8 @@ int main()
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void Worker::operator()(int device_id) const
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void Worker::operator()(int device_id) const
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{
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{
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// Set proper context
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// Set the proper context
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cuSafeCall(cuCtxPushCurrent(contexts[device_id]));
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safeCall(cuCtxPushCurrent(contexts[device_id]));
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Mat src(1000, 1000, CV_32F);
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Mat src(1000, 1000, CV_32F);
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Mat dst;
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Mat dst;
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@ -93,15 +105,15 @@ void Worker::operator()(int device_id) const
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// CPU works
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// CPU works
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transpose(src, dst);
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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_src(src);
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GpuMat d_dst;
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GpuMat d_dst;
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// GPU works
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transpose(d_src, d_dst);
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transpose(d_src, d_dst);
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// Check results
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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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bool passed = norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
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cout << "GPU #" << device_id << ": "<< (passed ? "passed" : "FAILED") << endl;
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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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// Deallocate data here, otherwise deallocation will be performed
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// after context is extracted from the stack
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// after context is extracted from the stack
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@ -109,14 +121,14 @@ void Worker::operator()(int device_id) const
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d_dst.release();
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d_dst.release();
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CUcontext prev_context;
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CUcontext prev_context;
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cuSafeCall(cuCtxPopCurrent(&prev_context));
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safeCall(cuCtxPopCurrent(&prev_context));
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}
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}
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void destroyContexts()
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void destroyContexts()
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{
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{
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cuSafeCall(cuCtxDestroy(contexts[0]));
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safeCall(cuCtxDestroy(contexts[0]));
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cuSafeCall(cuCtxDestroy(contexts[1]));
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safeCall(cuCtxDestroy(contexts[1]));
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}
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}
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#endif
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#endif
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183
samples/gpu/stereo_multi.cpp
Normal file
183
samples/gpu/stereo_multi.cpp
Normal file
@ -0,0 +1,183 @@
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/* This sample demonstrates working on one piece of data using two GPUs.
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It splits input into two parts and processes them separately on different
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GPUs. */
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// Disable some warnings which are caused with CUDA headers
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#pragma warning(disable: 4201 4408 4100)
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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/highgui/highgui.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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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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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 <cuda.h>
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#include <cuda_runtime.h>
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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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void destroyContexts();
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#define safeCall(code) if (code != CUDA_SUCCESS) { \
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cout << "CUDA driver API error: code " << code \
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<< ", file " << __FILE__ << ", line " << __LINE__ << endl; \
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destroyContexts(); \
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exit(-1); \
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}
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// Each GPU is associated with its own context
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CUcontext contexts[2];
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void inline contextOn(int id)
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{
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safeCall(cuCtxPushCurrent(contexts[id]));
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}
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void inline contextOff()
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{
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CUcontext prev_context;
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safeCall(cuCtxPopCurrent(&prev_context));
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}
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GpuMat d_left[2];
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GpuMat d_right[2];
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StereoBM_GPU* bm[2];
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GpuMat d_result[2];
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Mat result;
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int main(int argc, char** argv)
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{
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if (argc < 3)
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{
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cout << "Usage: stereo_multi_gpu <left_image> <right_image>\n";
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return -1;
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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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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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if (!DeviceInfo(i).isCompatible())
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{
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cout << "GPU module isn't built for GPU #" << i << " (" << DeviceInfo(i).name() << ")";
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return -1;
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}
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}
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// Load input data
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Mat left = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
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Mat right = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
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if (left.empty())
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{
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cout << "Cannot open '" << argv[1] << "'\n";
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return -1;
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}
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if (right.empty())
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{
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cout << "Cannot open '" << argv[2] << "'\n";
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return -1;
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}
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safeCall(cuInit(0));
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// Create context for the first GPU
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CUdevice device;
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safeCall(cuDeviceGet(&device, 0));
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safeCall(cuCtxCreate(&contexts[0], 0, device));
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contextOff();
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// Create context for the second GPU
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safeCall(cuDeviceGet(&device, 1));
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safeCall(cuCtxCreate(&contexts[1], 0, device));
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contextOff();
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// Split source images for processing on the first GPU
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contextOn(0);
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d_left[0].upload(left.rowRange(0, left.rows / 2));
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d_right[0].upload(right.rowRange(0, right.rows / 2));
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bm[0] = new StereoBM_GPU();
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contextOff();
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// Split source images for processing on the second GPU
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contextOn(1);
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d_left[1].upload(left.rowRange(left.rows / 2, left.rows));
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d_right[1].upload(right.rowRange(right.rows / 2, right.rows));
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bm[1] = new StereoBM_GPU();
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contextOff();
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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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// Release the first GPU resources
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contextOn(0);
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imshow("GPU #0 result", Mat(d_result[0]));
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d_left[0].release();
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d_right[0].release();
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d_result[0].release();
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delete bm[0];
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contextOff();
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// Release the second GPU resources
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contextOn(1);
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imshow("GPU #1 result", Mat(d_result[1]));
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d_left[1].release();
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d_right[1].release();
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d_result[1].release();
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delete bm[1];
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contextOff();
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waitKey();
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destroyContexts();
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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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contextOn(device_id);
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bm[device_id]->operator()(d_left[device_id], d_right[device_id],
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d_result[device_id]);
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cout << "GPU #" << device_id << " (" << DeviceInfo().name()
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<< "): finished\n";
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contextOff();
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}
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void destroyContexts()
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{
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safeCall(cuCtxDestroy(contexts[0]));
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safeCall(cuCtxDestroy(contexts[1]));
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}
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#endif
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