Unbutn20+cuda11+Qt下配置

心已赠人 2023-01-21 11:22 3阅读 0赞

首先装CUDA,具体查看其它博客，这里主要介绍如何配置。

CONFIG += console
    TARGET = test
    
    # Define output directories
    CUDA_OBJECTS_DIR = ./
    
    # This makes the .cu files appear in your project
    CUDA_SOURCES += \
        kernel.cu
    
    
    CUDA_DIR = "/usr/local/cuda"
    CUDA_RUNTIME = "/usr/local/cuda-11.0/targets/x86_64-linux"
    
    # CUDA settings
    SYSTEM_TYPE = 64                   # '32' or '64', depending on your system
    NVCC_OPTIONS = --use_fast_math
    
    #CUDA_ARCH = sm_50 # Type of CUDA architecture
    
    # https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/
    CUDA_ARCH = -gencode arch=compute_61,code=sm_61 \     # for Pascal architecture
                -gencode arch=compute_75,code=sm_75 \     # for Turing architecture
                -gencode arch=compute_75,code=compute_75
    
    # include paths
    INCLUDEPATH += $$CUDA_RUNTIME/include \
                   $$CUDA_DIR/include \
    
    # library directories
    CUDA_OBJECTS_DIR = ./
    CUDA_LIBS += -L$$CUDA_DIR/lib64 -lcudart -lcusolver -lcuda -lcufft -lcublas -lcurand -lcudnn -lnvidia-ml -lstdc++
    QMAKE_LIBDIR += $$CUDA_LIBS
    
    # The following makes sure all path names (which often include spaces) are put between quotation marks
    CUDA_INC = $$join(INCLUDEPATH,'" -I"','-I"','"')
    LIBS += $$CUDA_LIBS
    
    
    
    # Configuration of the Cuda compiler
    CONFIG(debug, debug|release) { 
        # Debug mode
        cuda_d.input = CUDA_SOURCES
        cuda_d.output = $$CUDA_OBJECTS_DIR/${ QMAKE_FILE_BASE}_cuda.obj
        cuda_d.commands = $$CUDA_DIR/bin/nvcc -D_DEBUG $$NVCC_OPTIONS $$CUDA_INC \
                          --machine $$SYSTEM_TYPE $$CUDA_ARCH \
                          -c -o ${ QMAKE_FILE_OUT} ${ QMAKE_FILE_NAME}
    
        cuda_d.dependency_type = TYPE_C
        QMAKE_EXTRA_COMPILERS += cuda_d
    }
    else { 
        # Release mode CUDA settings
        cuda_d.input = CUDA_SOURCES
        cuda_d.output = $$CUDA_OBJECTS_DIR/${ QMAKE_FILE_BASE}_cuda.obj
        cuda_d.commands = $$CUDA_DIR/bin/nvcc  $$NVCC_OPTIONS $$CUDA_INC  \
                          --machine $$SYSTEM_TYPE $$CUDA_ARCH \
                          -c -o ${ QMAKE_FILE_OUT} ${ QMAKE_FILE_NAME}
    
        cuda_d.dependency_type = TYPE_C
        QMAKE_EXTRA_COMPILERS += cuda_d
    }
    
    SOURCES += \
        main.cpp

然后创建两个file，main.cpp和kernal.cu  
main.cpp

#include <QtCore/QCoreApplication>
    
    extern "C" void runCudaPart();
    
    int main(int argc, char *argv[]) { 
      printf("hello");
      runCudaPart();
    }

kernal.cu

// CUDA-C includes
    #include <cuda.h>
    
    #include <cuda_runtime.h>
    
    #include <stdio.h>
    
    extern "C"
        //Adds two arrays
        void runCudaPart();
    
    
    __global__ void addAry( int * ary1, int * ary2 )
    { 
        int indx = threadIdx.x;
        ary1[ indx ] += ary2[ indx ];
    }
    
    
    // Main cuda function
    
    void runCudaPart() { 
    
        int ary1[32];
        int ary2[32];
        int res[32];
    
        for( int i=0 ; i<32 ; i++ )
        { 
            ary1[i] = i;
            ary2[i] = 2*i;
            res[i]=0;
        }
        int * d_ary1, *d_ary2;
        cudaMalloc((void**)&d_ary1, 32*sizeof(int));
        cudaMalloc((void**)&d_ary2, 32*sizeof(int));
        cudaMemcpy((void*)d_ary1, (void*)ary1, 32*sizeof(int), cudaMemcpyHostToDevice);
        cudaMemcpy((void*)d_ary2, (void*)ary2, 32*sizeof(int), cudaMemcpyHostToDevice);
        addAry<<<1,32>>>(d_ary1,d_ary2);
    
        cudaMemcpy((void*)res, (void*)d_ary1, 32*sizeof(int), cudaMemcpyDeviceToHost);
        for( int i=0 ; i<32 ; i++ )
            printf( "result[%d] = %d\n", i, res[i]);
        cudaFree(d_ary1);
        cudaFree(d_ary2);
    }

这是编译成功后的信息

21:37:56: Running steps for project cudalearn1...
    21:37:56: Configuration unchanged, skipping qmake step.
    21:37:56: Starting: "/usr/bin/make" -j12
    /opt/Qt5.13.0/5.13.0/gcc_64/bin/qmake -o Makefile ../cudalearn1/cudalearn1.pro -spec linux-g++ CONFIG+=qtquickcompiler
    /usr/local/cuda/bin/nvcc --use_fast_math -I"/usr/local/cuda-11.0/targets/x86_64-linux/include" -I"/usr/local/cuda/include" -L/usr/local/cuda/lib64 -lcudart -lcusolver -lcuda -lcufft -lcublas -lcurand -lcudnn -lnvidia-ml -lstdc++ --machine 64 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75 -c -o kernel_cuda.obj ../cudalearn1/kernel.cu
    g++ -c -pipe -O2 -Wall -W -D_REENTRANT -fPIC -DQT_NO_DEBUG -DQT_GUI_LIB -DQT_CORE_LIB -I../cudalearn1 -I. -I/usr/local/cuda-11.0/targets/x86_64-linux/include -I/usr/local/cuda/include -I/opt/Qt5.13.0/5.13.0/gcc_64/include -I/opt/Qt5.13.0/5.13.0/gcc_64/include/QtGui -I/opt/Qt5.13.0/5.13.0/gcc_64/include/QtCore -I. -isystem /usr/include/libdrm -I/opt/Qt5.13.0/5.13.0/gcc_64/mkspecs/linux-g++ -o main.o ../cudalearn1/main.cpp
    
    g++ -Wl,-O1 -Wl,-rpath,/opt/Qt5.13.0/5.13.0/gcc_64/lib -o test kernel_cuda.obj main.o   -L-L/usr/local/cuda/lib64 -L-lcudart -L-lcusolver -L-lcuda -L-lcufft -L-lcublas -L-lcurand -L-lcudnn -L-lnvidia-ml -L-lstdc++ -L/usr/local/cuda/lib64 -lcudart -lcusolver -lcuda -lcufft -lcublas -lcurand -lcudnn -lnvidia-ml -lstdc++ /opt/Qt5.13.0/5.13.0/gcc_64/lib/libQt5Gui.so /opt/Qt5.13.0/5.13.0/gcc_64/lib/libQt5Core.so -lGL -lpthread   
    21:37:57: The process "/usr/bin/make" exited normally.
    21:37:57: Elapsed time: 00:01.

运行的时候只能在release下运行，有些童鞋在**debug下一运行，程序就崩溃**，就是在qt下debug运行内存段错误了，非要debug下运行，那么就在debug编译后去命令行去运行。