another.context=nullptr;this->devptrs = std::map<std::string, CUdeviceptr>(std::move(another.devptrs));this->modules = std::map<std::string, CUmodule>(std::move(another.modules));return*this; }virtual~Cuder(){ release(); };public:boollaunch(dim3 gridDim, dim3 blockDim, std::st...
std::cerr << "cudaGetDeviceProperties returned " << static_cast<int>(error) << ": " << cudaGetErrorString(error) << std::endl; return 1; } std::cout << "Device " << device << ": " << deviceProp.name << std::endl; std::cout << " asyncEngineCount: " << deviceProp.a...
std::string error_message;// Add vectors in parallel.cudaError_t cuda_status =addWithCuda(c, a, b, arraySize, &error_message);if(cuda_status != cudaSuccess) {UE_LOG(LogTemp, Warning,TEXT("addWithCuda failed!\n"));UE_LOG(LogTemp, Warning,TEXT("%s"), *FString(error_message.c_st...
std::vector不是viewer,因为他是一个容器,具有内存的拥有权。 std::span是viewer,他不是容器,他只是对容器的一个切片。 std::string不是viewer,因为他是一个容器,具有内存的拥有权 std::string_view是viewer,他不是容器,他只是对容器的一个切片。 当然以上所谓的viewer并不能在GPU上访问,muda会要求viewer能够...
template < typename T >nvrtcResult nvrtcGetTypeName ( std::string* result ) nvrtcGetTypeName stores the source level name of the template type argument T in the given std::string location. nvrtcResult nvrtcGetTypeName ( const std::type_info& tinfo, std::string* result ) nvr...
void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector<std::string> obj_names, int current_det_fps = -1, int current_cap_fps = -1) { int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } }; for ...
std::string getKernelNameForType(void) { // Look up the source level name string for the type "T" using // nvrtcGetTypeName() and use it to create the kernel name std::string type_name; NVRTC_SAFE_CALL(nvrtcGetTypeName(&type_name)); return std::string("f3<") + type_name + "...
std::string texStr;for (unsigned int i = 0; i < m_blurTimes; ++i){ m_framebuffer[m_writeIndex]->bind(); glDisable(GL_CULL_FACE); glDisable(GL_DEPTH_TEST); glDisable(GL_BLEND); glClearColor(0.0, 0.0, 1.0, 1.0); glClear(GL_COLOR_BUFFER_BIT); blurShader->setInt("Color", ...
(int), cudaMemcpyDeviceToHost); for (int ii = 1; ii < numOutputElements; ii++) { hostOutput[0] += hostOutput[ii]; //accumulates the sum in the first element } int sumGPU = hostOutput[0]; printf("GPU Result: %d\n", sumGPU); std::string wait; std::cin >> wait; return ...
fprintf(stderr, "runMaxFlow_GPU launch failed: %s\n", cudaGetErrorString(cudaStatus)); goto Error; } // cudaDeviceSynchronize waits for the kernel to finish, and returns // any errors encountered during the launch. cudaStatus = cudaDeviceSynchronize(); ...