Cuda编程总结--cuda c programming Guide

Cuda编程总结2013-10-120:32:46

--------余家奎

 

参加书籍：NVIDIACUDA C Programming Guide

OpenGL编程指南

 

学习cuda例子中的总结

1、__constant__和__device__,__shared__的使用说明...2

2、分配二位数组实现两个二位数组相加...2

3、用cudaMemcpyPitch和cudaMemcpy2D实现二位数组的分配和拷贝...5

4、cudaMalloc3D()和cudaMemcpy3D()函数的用法...7

5、不带共享存储器的矩阵的相乘...9

6、带shared memory的矩阵相乘...12

7、页锁定主机存储器Page-locked Host memory.16

8、纹理存储的使用texture memory.18

9、surface Memory的使用方法...20

10、opengl和cuda的交互...22

11、Formatted output---printf函数在device的函数中，但是其需要其的compute copability至少为2.0 27

12、Asserting在设备端的函数中，但是其要求其计算能力至少为2.0.28

13、Per Thread Allocation On heap每个线程在堆上分配...29

14、Per Thread Block Allocation每个线程块在堆上分配空间...29

15、Allocation Persisting Between Kernel Launches在堆上分配...30 

如有错误的地方还请指正。。。谢谢

 

1、__constant__和__device__,__shared__的使用说明

其对应的程序：

//

#include "cuda_runtime.h"

#include "device_launch_parameters.h"

#include

#include  

// __constant__ int device_global_var=5;

// __device__ int device_global_var=5;

__shared__ int device_global_var ;

__global__ void kernel()

{

    __shared__ int xx;

}

int main()

{   

      int host_var=5;

    cudaMemcpyToSymbol(device_global_var,&host_var,sizeof(int)); 

    printf("value=%d\n",host_var);   

    cudaMemcpyFromSymbol(&host_var,device_global_var,sizeof(int));

    printf("device_value=%d\n",host_var); 

    system("pause");

    return 0;

} 

2、分配二位数组实现两个二位数组相加

#include "cuda_runtime.h"

#include "device_launch_parameters.h" 

#include

#include  

#define  N 16 

__device__ intdevice_a[N][N],device_b[N][N],device_c[N][N]; 

__global__ void VecAdd(inta[N][N],int b[N][N],intc[N][N])

{

    int global_threadId_x=blockIdx.x*blockDim.x+threadIdx.x;

    int global_threadId_y=blockIdx.y*blockDim.y+threadIdx.y; 

    if (global_threadId_x

    {

        c[global_threadId_y][global_threadId_x]=a[global_threadId_y][global_threadId_x]+

            b[global_threadId_y][global_threadId_x];

    }

}

void printfArray(int data[N][N])

{

    for (inti=0;i

    {

        for (intj=0;j

        {

            printf("%d ",data[i][j]);

        }

        printf("\n");

    }

} 

void host_Add(int a[N][N],int b[N][N],intc[N][N])

{

    for (inti=0;i

    {

        for (int j=0;j

        {

            c[i][j]=a[i][j]+b[i][j];

        }

    }

}

int main()

{

    int i,j;

    int k=0;

    int a[N][N],b[N][N];

    int c[N][N];   

    for (i=0;i

    {

        for (j=0;j

        {

            a[i][j]=k;

            b[i][j]=k;

            k++;

        }

    }

    int tempA[N][N];

    //int (*device_aa)[N];

    int **device_aa;

    cudaMalloc((void**)&device_aa,sizeof(int)*N*N);

    cudaMemcpyToSymbol(device_a,a,sizeof(int)*N*N); 

    cudaMemcpyFromSymbol(tempA,device_a,sizeof(int)*N*N);

    printf("tempA====\n");

    printfArray(tempA);  

    system("pause");

    return 0;

} 

3、用cudaMemcpyPitch和cudaMemcpy2D实现二位数组的分配和拷贝

代码：

#include "cuda_runtime.h"

#include "device_launch_parameters.h"

#include

#include

#include  

// kernel which copies data from d_arrayto destinationArray 

__global__ void CopyData(float*d_array, 

                                   float* destinationArray, 

                                   size_tpitch, 

                                   int columnCount, 

                                   int rowCount) 

{ 

  for (int row = 0; row< rowCount; row++) 

  { 

     // update the pointer to point to the beginning of the nextrow 

     float* rowData = (float*)(((char*)d_array) +(row * pitch)); 

    for (int column = 0;column < columnCount; column++) 

    { 

      rowData[column] =123.0; // make every value in the array123.0 

     destinationArray[(row*columnCount) + column] = rowData[column]; 

    } 

  } 

} 

int main(int argc,char**argv) 

{ 

  int columnCount = 15; 

  int rowCount = 10; 

  float* d_array; // thedevice array which memory will be allocated to 

  float* d_destinationArray; //the device array 

  // allocate memory on the host 

  float* h_array = new float[columnCount*rowCount]; 

  // the pitch value assigned by cudaMallocPitch 

  // (which ensures correct data structure alignment) 

  size_tpitch; 

  //allocated the device memory for source array 

  cudaMallocPitch(&d_array, &pitch,columnCount * sizeof(float), rowCount); 

  //allocate the device memory for destination array 

  cudaMalloc(&d_destinationArray,columnCount*rowCount*sizeof(float)); 

  //call the kernel which copies values from d_array tod_destinationArray 

 CopyData<<<100, 512>>>(d_array, d_destinationArray,pitch, columnCount, rowCount); 

  //copy the data back to the host memory  

  float *h_result=(float*)malloc(sizeof(float)*columnCount*rowCount);

 memset(h_result,0,sizeof(float)*columnCount*rowCount); 

 cudaMemcpy2D(h_result,columnCount*sizeof(float),d_array,pitch,columnCount*sizeof(float),rowCount,cudaMemcpyDeviceToHost);

 

 cudaMemcpy(h_array, 

                   d_destinationArray, 

                   columnCount*rowCount*sizeof(float), 

                   cudaMemcpyDeviceToHost);  

  for(int i = 0 ; i< rowCount ; i++) 

  { 

      for(int j = 0 ; j < columnCount ; j++) 

      { 

          cout << "h_result["<< (i*columnCount) + j <<"]="<< h_result[(i*columnCount) + j] << endl; 

      } 

  } 

  system("pause");

  printf("h_array==\n");

  //print out the values (all the values are 123.0) 

  for(int i = 0 ; i< rowCount ; i++) 

  { 

    for(int j = 0 ; j< columnCount ; j++) 

    { 

      cout<< "h_array[" <<(i*columnCount) + j <<"]="<< h_array[(i*columnCount) + j] << endl; 

    } 

  }  

  system("pause");

}  

4、cudaMalloc3D()和cudaMemcpy3D()函数的用法

代码：

#include "cuda_runtime.h"

#include "device_launch_parameters.h" 

#include

#include  

// Device code

__global__ void MyKernel(cudaPitchedPtrdevPitchedPtr,cudaExtent extent)

{

    char* devPtr = (char*)devPitchedPtr.ptr;

    size_t pitch= devPitchedPtr.pitch;

    size_tslicePitch = pitch * extent.height;

    for(int k=0; k

        char* slice = devPtr + k * slicePitch;

        for(int j=0; j

            float3* row = (float3*)(slice+j*pitch); 

            for (inti=0;i

            {

                row[i].x=2;

                row[i].y=3;

                row[i].z=4;

            }

        }

    } 

} 

const int x=6;

const int y=60;

const int z=66; 

int main(){

    size_tbuf_pf=900000000;

//  cudaPrintfInit(buf_pf);

    cudaError_tstatus = cudaSuccess; 

    //======== Mem Host 

    float3 *mem_host = (float3*)malloc(sizeof(float3)*x*y*z);

    float3 *mem_host2 = (float3*)malloc(sizeof(float3)*x*y*z); 

    for(int i=0;i

        mem_host[i].x=10;

        mem_host[i].y=100;

        mem_host[i].z=1000;

    }

    //======== Mem Device 

    cudaExtentextent;

    extent.width=x*sizeof(float3);

    extent.height=y;

    extent.depth=z; 

    cudaPitchedPtrmem_device;

    status=cudaMalloc3D(&mem_device,extent);

// if(status!= cudaSuccess){fprintf(stderr, "Malloc: %s\n", cudaGetErrorString(status));}

//

// //========Cpy HostToDevice

//

// cudaMemcpy3DParmsp = { 0 };

// p.srcPtr= make_cudaPitchedPtr((void*)mem_host, x*sizeof(float3),x,y);

// p.dstPtr= mem_device;

// p.extent= extent;

// p.kind= cudaMemcpyHostToDevice;

// status=cudaMemcpy3D(&p);

// if(status!= cudaSuccess){fprintf(stderr, "MemcpyHtD: %s\n",cudaGetErrorString(status));} 

    MyKernel<<<1,1>>>(mem_device,extent); 

    //======== Cpy DeviceToHost !!!!!!! UNTESTED !!!!!!!! 

    cudaMemcpy3DParmsq = {0};

    q.srcPtr =mem_device;

    q.dstPtr =make_cudaPitchedPtr((void*)mem_host2,x*sizeof(float3),x,y);

    q.extent=extent;

    q.kind =cudaMemcpyDeviceToHost;

    status=cudaMemcpy3D(&q);

    if(status != cudaSuccess){fprintf(stderr,"MemcpyDtoH: %s\n",cudaGetErrorString(status));} 

    for(int i=0;i

        printf("%f %f %f\n",mem_host2[i].x,mem_host2[i].y,mem_host2[i].z); 

    cudaFree(mem_device.ptr); 

    system("pause"); 

}

5、不带共享存储器的矩阵的相乘

代码：

#include "cuda_runtime.h"

#include "device_launch_parameters.h" 

#include

#include

#include  

typedef struct 

{

    int width;

    int height;

    float *element;

}Matrix; 

#define BLOCK_SIZE 16 

__global__ void MatMulKernel(const Matrix,const Matrix,Matrix); 

void printMatrix(const Matrix &A)

{

    for (inti=0;i

    {

        for (intj=0;j

        {

            printf("%f ",A.element[i*A.width+j]);

        }

        printf("\n");

    }

}

void MatMul(const Matrix &A,const Matrix &B,Matrix &C)

{

    printf("matrix A");

    printMatrix(A);

    printf("matrix B");

    printMatrix(B);

    system("pause"); 

    Matrix d_A;

    d_A.width=A.width;

    d_A.height=A.height;

    size_tsize=A.width*A.height*sizeof(float);

    cudaMalloc(&d_A.element,size);

    cudaMemcpy(d_A.element,A.element,size,cudaMemcpyHostToDevice); 

    Matrix d_B;

    d_B.width=B.width;

    d_B.height=B.height;

    size=B.width*B.height*sizeof(float);

    cudaMalloc(&d_B.element,size);

    cudaMemcpy(d_B.element,B.element,size,cudaMemcpyHostToDevice); 

    Matrix d_C;

    d_C.width=C.width;

    d_C.height=C.height;

    size=C.width*C.height*sizeof(float);

    cudaMalloc(&d_C.element,size);   

    dim3 dimBlock(BLOCK_SIZE,BLOCK_SIZE);

    dim3 dimGrid(B.width/dimBlock.x,A.height/dimBlock.y);

    MatMulKernel<<>>(d_A,d_B,d_C); 

    cudaMemcpy(C.element,d_C.element,size,cudaMemcpyDeviceToHost); 

    cudaFree(d_A.element);

    cudaFree(d_B.element);

    cudaFree(d_C.element); 

    for (inti=0;i

    {

        for (intj=0;j

        {

            printf("%f ",C.element[i*C.width+j]);

        }

        printf("\n");

    }

    system("pause");

}

void SetMatrixValue(Matrix &A,int value)

{

    for (inti=0;i

    {

        for (intj=0;j

        {

            A.element[i*A.width+j]=value;

        }

    }

}

void main()

{

    MatrixA,B,C;

    A.width=128;

    A.height=128;

    A.element=(float*)malloc(A.width*A.height*sizeof(float));

    SetMatrixValue(A,2); 

    B.width=128;

    B.height=128;

    B.element=(float*)malloc(B.width*B.height*sizeof(float));

    //memset(B.element,2,sizeof(float)*B.width*B.height);

    SetMatrixValue(B,2); 

    C.width=128;

    C.height=128;

    C.element=(float*)malloc(C.width*C.height*sizeof(float));

    //memset(C.element,2,sizeof(float)*C.width*C.height); 

    MatMul(A,B,C); 

    for (inti=0;i

    {

        for (intj=0;j

        {

            printf("%f ",C.element[i*C.width+j]);

        }

        printf("\n");

    } 

    system("pause");

} 

__global__ void MatMulKernel(Matrix A,MatrixB,Matrix C)

{

    float CValue=0;

    int row=blockIdx.y*blockDim.y+threadIdx.y;

    int col=blockIdx.x*blockDim.x+threadIdx.x; 

    for (inte=0;e

    {

        CValue+=A.element[row*A.width+e]*B.element[e*B.width+col];

    } 

    C.element[row*C.width+col]=CValue;

} 

6、带shared memory的矩阵相乘

代码：

#include "cuda_runtime.h"

#include "device_launch_parameters.h" 

#include

#include  

#define BLOCK_SIZE 16 

typedef struct

{

    int width;

    int height;

    int stride;

    float *elements;

}Matrix; 

__device__ float GetElement(constMatrix A, int row,intcol)

{

    return A.elements[row*A.stride+col];

} 

__device__ void SetElement(Matrix A,int row,int col,float value)

{

    A.elements[row*A.stride+col]=value;

} 

__device__ Matrix GetSubMatrix(Matrix A,int row,int col)

{

    Matrix Asub;

    Asub.width=BLOCK_SIZE;

    Asub.height=BLOCK_SIZE;

    Asub.stride=A.stride;

    Asub.elements=&A.elements[A.stride*BLOCK_SIZE*row+BLOCK_SIZE*col]; 

    return Asub;

} 

__global__ void MatMulKernel(const Matrix,const Matrix,Matrix); 

void MatMul(const Matrix &A,const Matrix &B,Matrix &C)

{

    Matrix d_A;

    d_A.width=A.width;

    d_A.height=A.height;

    d_A.stride=A.width;

    size_tsize=d_A.width*d_A.height*sizeof(float);

    cudaMalloc(&d_A.elements,size);

    cudaMemcpy(d_A.elements,A.elements,size,cudaMemcpyHostToDevice); 

    Matrix d_B;

    d_B.width=B.width;

    d_B.height=B.height;

    d_B.stride=B.width;

    size=B.width*B.height*sizeof(float);

    cudaMalloc(&d_B.elements,size);

    cudaMemcpy(d_B.elements,B.elements,size,cudaMemcpyHostToDevice); 

    Matrix d_C;

    d_C.width=C.width;

    d_C.height=C.height;

    d_C.stride=C.width;

    size=C.width*C.height*sizeof(float);

    cudaMalloc(&d_C.elements,size); 

    dim3 dimBlock(BLOCK_SIZE,BLOCK_SIZE);

    dim3 dimGrid(B.width/BLOCK_SIZE,B.height/BLOCK_SIZE);

    MatMulKernel<<>>(d_A,d_B,d_C); 

    cudaMemcpy(C.elements,d_C.elements,size,cudaMemcpyDeviceToHost);

    cudaFree(d_A.elements);

    cudaFree(d_B.elements);

    cudaFree(d_C.elements);

} 

__global__ void MatMulKernel(Matrix A,MatrixB,Matrix C)

{

    int blockRow=blockIdx.y;

    int blockCol=blockIdx.x; 

    MatrixCsub=GetSubMatrix(C,blockRow,blockCol); 

    float Cvalue=0; 

    int row=threadIdx.y;

    int col=threadIdx.x; 

    for (intm=0;m

    {

        MatrixAsub=GetSubMatrix(A,blockRow,m);

        MatrixBsub=GetSubMatrix(B,m,blockCol); 

        __shared__ floatAs[BLOCK_SIZE][BLOCK_SIZE];

        __shared__ floatBs[BLOCK_SIZE][BLOCK_SIZE]; 

        As[row][col]=GetElement(Asub,row,col);

        Bs[row][col]=GetElement(Bsub,row,col); 

        __syncthreads(); 

        for (inte=0;e

        {

            Cvalue+=As[row][e]*Bs[e][col];

        } 

        __syncthreads();

    } 

    SetElement(Csub,row,col,Cvalue);

}

 void SetMatValue(Matrix A,int value)

{

    for (inti=0;i

    {

        for (intj=0;j

        {

            A.elements[i*A.width+j]=value;

        }

    }

}

void PrintMat(const Matrix A)

{

    for (int i=0;i

    {

        for(intj=0;j

        {

            printf("%f ",A.elements[i*A.width+j]);

        }

        printf("\n");

    }

}

void main()

{

    MatrixA,B,C;

    A.width=128;

    A.height=128;

    A.elements=(float*)malloc(A.width*A.height*sizeof(float));

    SetMatValue(A,2); 

    B.width=128;

    B.height=128;

    B.elements=(float*)malloc(B.width*B.height*sizeof(float));

    SetMatValue(B,2); 

    C.width=128;

    C.height=128;

    C.elements=(float *)malloc(C.width*C.height*sizeof(float)); 

    MatMul(A,B,C);

    PrintMat(C);

    system("pause"); 

} 

7、页锁定主机存储器Page-lockedHost memory

代码：

#include "cuda_runtime.h"

#include "device_launch_parameters.h" 

#include

#include  

#define N 256 

void SetArrayValue(float *pData,int size,float value)

{

    for (inti=0;i

    {

        pData[i]=value;

    }

} 

__global__ void VecAdd(float*A,float *B,float*C)

{

    int index=threadIdx.x;

    C[index]=A[index]+B[index];

} 

void PrintArray(float *data,int size)

{

    for(inti=0;i

    {

        if((i+1)%10==0)

        {

            printf("\n");

        }

        printf("%f ",data[i]);

    }

} 

void main()

{

    //cudaSetDeviceFlags(cudaDeviceMapHost);---可有可无

    cudaDevicePropdeviceProp;

    cudaGetDeviceProperties(&deviceProp,0); 

    if (deviceProp.integrated)

    {

        printf("GPU is integrated\n");

        return;

    }

    if(!deviceProp.canMapHostMemory)

    {

        printf("can't map host memory\n");

        return;

    }   

    float *hostPtrA;

    cudaHostAlloc(&hostPtrA,sizeof(float)*N,cudaHostAllocDefault| cudaHostAllocMapped);

    SetArrayValue(hostPtrA,N,29); 

    PrintArray(hostPtrA,N); 

    system("pause"); 

    float *hostPtrB;

    cudaHostAlloc(&hostPtrB,sizeof(float)*N,cudaHostAllocDefault| cudaHostAllocMapped);

    SetArrayValue(hostPtrB,N,31); 

    float *devPtrA,*devPtrB;

    cudaHostGetDevicePointer(&devPtrA,hostPtrA,0);

    cudaHostGetDevicePointer(&devPtrB,hostPtrB,0); 

    float *hostPtrC;

    cudaHostAlloc(&hostPtrC,sizeof(float)*N,cudaHostAllocDefault| cudaHostAllocMapped);

    float *devPtrC;

    cudaHostGetDevicePointer(&devPtrC,hostPtrC,0); 

    VecAdd<<<1,N>>>(devPtrA,devPtrB,devPtrC); 

    cudaDeviceSynchronize(); 

    for(inti=0;i

    {

        if ((i+1)%10==0)

        {

            printf("\n");

        }

        printf("%f ",hostPtrC[i]);

    }

    system("pause");

}

8、纹理存储的使用texturememory

代码：

#include "cuda_runtime.h"

#include "device_launch_parameters.h"

#include

#include  

#define size 256 

texture<float,cudaTextureType2D,cudaReadModeElementType>texRef; 

__global__ void transformKernel(float *output,intwidth,int height,floattheta)

{

    unsigned int x=blockIdx.x*blockDim.x+threadIdx.x;

    unsigned int y=blockIdx.y*blockDim.y+threadIdx.y; 

    float u=x/(float)width;

    float v=y/(float)height; 

    u-=0.5f;

    v-=0.5f;

    float tu=u*cosf(theta)-v*sin(theta)+0.5f;

    float tv=v*cosf(theta)+u*sinf(theta)+0.5f;

    output[y*width+x]=tex2D(texRef,tu,tv);

} 

void main()

{

    int width=25,height=25;

    cudaChannelFormatDescchannelDesc=cudaCreateChannelDesc(32,0,0,0,cudaChannelFormatKindFloat);

    cudaArray*cuArray;

    cudaMallocArray(&cuArray,&channelDesc,width,height); 

    float *h_data=(float*)malloc(width*height*sizeof(float));

    for (inti=0;i

    {

        for (intj=0;j

        {

            h_data[i*width+j]=i*width+j;

        }

    } 

    cudaMemcpyToArray(cuArray,0,0,h_data,width*height*sizeof(float),cudaMemcpyHostToDevice); 

    texRef.addressMode[0]=cudaAddressModeWrap;

    texRef.addressMode[1]=cudaAddressModeWrap;

    texRef.filterMode=cudaFilterModeLinear;

    texRef.normalized=true; 

    cudaBindTextureToArray(texRef,cuArray,channelDesc);

    float *output;

    cudaMalloc(&output,width*height*sizeof(float)); 

    dim3 dimBlock(16,16);

    dim3dimGrid((width+dimBlock.x-1)/dimBlock.x,(height+dimBlock.y-1)/dimBlock.y);

    float angle=30; 

    transformKernel<<>>(output,width,height,angle); 

    float *hostPtr=(float*)malloc(sizeof(float)*width*height);

    cudaMemcpy(hostPtr,output,sizeof(float)*width*height,cudaMemcpyDeviceToHost); 

    for (inti=0;i

    {

        for (intj=0;j

        {

            printf("%f ",hostPtr[i*width+j]);

        }

        printf("\n");

    } 

    free(hostPtr); 

    cudaFreeArray(cuArray);

    cudaFree(output); 

    system("pause");

} 

9、surface Memory的使用方法

代码：

#include "cuda_runtime.h"

#include "device_launch_parameters.h"

#include

#include  

surface<void,2> inputSurfRef;

surface<void,2>outputSurfRef; 

__global__ void copyKernel(intwidth,int height)

{

    unsigned int x=blockIdx.x*blockDim.x+threadIdx.x;

    unsigned int y=blockIdx.y*blockDim.y+threadIdx.y; 

    if(x

    {

        uchar4 data;

        surf2Dread(&data,inputSurfRef,x*4,y);

        surf2Dwrite(data,outputSurfRef,x*4,y);

    }

}

void main()

{

    int width=256,height=256;

    unsigned int*h_data=(unsigned int*)malloc(width*height*sizeof(unsignedint));

    for (inti=0;i

    {

        for (intj=0;j

        {

            h_data[i*width+j]=3;

        }

    }

    int size=width*height*sizeof(unsignedint); 

    cudaChannelFormatDescchannelDesc=cudaCreateChannelDesc(8,8,8,8,cudaChannelFormatKindUnsigned);

    cudaArray*cuInputArray;

    cudaMallocArray(&cuInputArray,&channelDesc,width,height,cudaArraySurfaceLoadStore); 

    cudaArray*cuOutputArray;

    cudaMallocArray(&cuOutputArray,&channelDesc,width,height,cudaArraySurfaceLoadStore); 

    cudaMemcpyToArray(cuInputArray,0,0,h_data,size,cudaMemcpyHostToDevice); 

    cudaBindSurfaceToArray(inputSurfRef,cuInputArray);

    cudaBindSurfaceToArray(outputSurfRef,cuOutputArray); 

    dim3 dimBlock(16,16);

    dim3dimGrid((width+dimBlock.x-1)/dimBlock.x,(height+dimBlock.y-1)/dimBlock.y); 

    copyKernel<<>>(width,height); 

    unsigned int*host_output=(unsigned int*)malloc(sizeof(unsignedint)*width*height);

    cudaMemcpyFromArray(host_output,cuOutputArray,0,0,size,cudaMemcpyDeviceToHost); 

    for (inti=0;i

    {

        for (intj=0;j

        {

            printf("%u ",host_output[i*width+j]);

        }

        printf("\n");

    } 

    system("pause");

    free(host_output);

    free(h_data);

    cudaFreeArray(cuInputArray);

    cudaFreeArray(cuOutputArray);

}

 

10、opengl和cuda的交互

代码：https://devtalk.nvidia.com/default/topic/502692/how-to-use-open_gl/

http://stackoverflow.com/questions/12082357/errors-while-using-opengl-buffers-using-visual-studio-2010-in-windows7 

#include

#include "cuda_runtime.h"

#include "device_launch_parameters.h"

#include //要放在下面这一句的前面

#include "cuda_gl_interop.h" 

#include

#include  

//#include

#include  

GLuint postionsVBO=1;

struct cudaGraphicsResource * postionsVBO_CUDA; 

int width=256;

int height=256;

__device__ float dev_time=1;

float host_time=1; 

__global__ void createVertices(float4 *positions,floattime,unsignedintwidth ,unsignedintheight); 

void init()

{

    glClearColor(0.f,0.f,0.f,1.f);

    glClear(GL_DEPTH_BUFFER_BIT| GL_COLOR_BUFFER_BIT);

} 

void reshape(int width,int height)

{

    glMatrixMode(GL_PROJECTION);

    glLoadIdentity();

    if (width>height)

    {

        gluPerspective(45,(GLfloat)width/height,0.001,1000);

    }else

    {

        gluPerspective(45,(GLfloat)height/width,0.001,1000);

    }

    glMatrixMode(GL_MATRIX_MODE);

    glLoadIdentity();   

}

void display()

{

    float4 *positions;

    cudaGraphicsMapResources(1,&postionsVBO_CUDA,0);

    size_tnumb_bytes;

    cudaGraphicsResourceGetMappedPointer((void**)&positions,&numb_bytes,postionsVBO_CUDA); 

    dim3 dimBlock(16,16,1);

    dim3 dimGrid(width/dimBlock.x,height/dimBlock.y,1);

    createVertices<<>>(positions,dev_time,width,height); 

    dev_time++;

    cudaMemcpy(&time,&host_time,sizeof(float),cudaMemcpyHostToDevice); 

    cudaGraphicsUnmapResources(1,&postionsVBO_CUDA,0); 

    glClear(GL_COLOR_BUFFER_BIT| GL_DEPTH_BUFFER_BIT);

    glBindBuffer(GL_ARRAY_BUFFER,postionsVBO);

    glVertexPointer(4,GL_FLOAT,0,0);

    glEnableClientState(GL_VERTEX_ARRAY);

    glDrawArrays(GL_POINTS,0,width*height);

    glDisableClientState(GL_VERTEX_ARRAY); 

    glutSwapBuffers();

    glutPostRedisplay();

} 

__global__ void createVertices(float4 *positions,floattime,unsignedintwidth ,unsignedintheight)

{

    unsigned int x=blockIdx.x*blockDim.x+threadIdx.x;

    unsigned int y=blockIdx.y*blockDim.y+threadIdx.y; 

    float u=x/(float)width;

    float v=y/(float)height; 

    u=u*2.f-1.f;

    v=v*2.f-1.f; 

    float freq=4.f;

    float w=sinf(u*freq+time)*cosf(v*freq+time)*0.5f; 

    positions[y*width+x]=make_float4(u,w,v,1.f); 

} 

int main(int argc,char*argv[])

{

    cudaGLSetGLDevice(0); 

    glutInit(&argc,argv);

    glutInitDisplayMode(GLUT_DOUBLE| GLUT_RGB);

    glutInitWindowPosition(0,0);

    glutInitWindowSize(100,100);

    glutCreateWindow("opengl-cuda");

    init();

    glutDisplayFunc(display);

    glutReshapeFunc(reshape); 

    glewInit();//http://stackoverflow.com/questions/12344612/unusual-error-using-opengl-buffers-with-cuda-interop-on-ms-visual-studio-2010 

    glGenBuffers(1,&postionsVBO);

    glBindBuffer(GL_ARRAY_BUFFER,postionsVBO);

    unsigned intsize=width*height*4*sizeof(float);

    glBufferData(GL_ARRAY_BUFFER,size,0,GL_DYNAMIC_DRAW);

    glBindBuffer(GL_ARRAY_BUFFER,0);

    cudaGraphicsGLRegisterBuffer(&postionsVBO_CUDA,postionsVBO,cudaGraphicsMapFlagsWriteDiscard); 

    glutMainLoop();

}

11、Formattedoutput---printf函数在device的函数中，但是其需要其的compute copability至少为2.0

代码：

#include

#include

#include "cuda_runtime.h"

#include "device_launch_parameters.h" 

__global__ void bcast()

{

    printf("%d\n",threadIdx.x);

} 

void main()

{

    bcast<<<1,32>>>(); 

    cudaDeviceSynchronize();

    system("pause");

} 

12、Asserting在设备端的函数中，但是其要求其计算能力至少为2.0

代码：

#include

#include  

#include "cuda_runtime.h"

#include "device_launch_parameters.h"

#include

#include  

__global__ void testAssert(void)

{

    int is_one=1;

    int should_be_one=0; 

    assert(is_one);

    assert(should_be_one);

} 

void main()

{

    testAssert<<<1,1>>>();

    cudaDeviceSynchronize();

    cudaDeviceReset();

    system("pause");

} 

13、Per ThreadAllocation On heap每个线程在堆上分配

代码：

#include

#include

#include

#include

#include "cuda_runtime.h"

#include "device_launch_parameters.h" 

__global__ void mallocTestPerThread()

{

    char *ptr=(char *)malloc(100);

    printf("Thread %d got pointer:%p\n",threadIdx.x,ptr);

    free(ptr);

} 

int main()

{

    cudaDeviceSetLimit(cudaLimitMallocHeapSize,128*1024*1024);

    mallocTestPerThread<<<1,5>>>();

    cudaDeviceSynchronize(); 

    system("pause");

    return 0;

}

14、Per Thread BlockAllocation每个线程块在堆上分配空间

代码：

#include

#include

#include

#include

#include "cuda_runtime.h"

#include "device_launch_parameters.h" 

__global__ void mallocTestPerThreadBlock()

{

    __shared__ int *data;

    if (threadIdx.x==0)

    {

        data=(int*)malloc(blockDim.x*64);

    }

    __syncthreads(); 

    if (data==NULL)

    {

        return;

    } 

    int *ptr=data;

    for (inti=0;i<64;++i)

    {

        ptr[i*blockDim.x+threadIdx.x]=threadIdx.x;

    }

    __syncthreads(); 

    if (threadIdx.x==0)

    {

        free(data);

    }

} 

int main()

{

    cudaDeviceSetLimit(cudaLimitMallocHeapSize,128*1024*1024);

    mallocTestPerThreadBlock<<<10,128>>>();

    cudaDeviceSynchronize(); 

    system("pause");

    return 0;

}

15、AllocationPersisting Between Kernel Launches在堆上分配

代码：

#include

#include

#include

#include

#include "cuda_runtime.h"

#include "device_launch_parameters.h" 

#define NUM_BLOCKS 20 

__device__ int *dataptr[NUM_BLOCKS]; 

__global__ void allocmem()

{

    if (threadIdx.x==0)

    {

        dataptr[blockIdx.x]=(int*)malloc(blockDim.x*4);

    }

    __syncthreads(); 

    if (dataptr[blockIdx.x]==NULL)

    {

        return;

    } 

    dataptr[blockIdx.x][threadIdx.x]=0;

} 

__global__ void usemem()

{

    int *ptr=dataptr[blockIdx.x];

    if (ptr!=NULL)

    {

        ptr[threadIdx.x]+=threadIdx.x;

    }

} 

__global__ void freemem()

{

    int *ptr=dataptr[blockIdx.x];

    if(ptr!=NULL)

        printf("Block %d,Thread=%d:final value=%d\n",blockIdx.x,threadIdx.x,ptr[threadIdx.x]); 

    if (threadIdx.x==0)

    {

        free(ptr);

    }

} 

int main()

{

    cudaDeviceSetLimit(cudaLimitMallocHeapSize,128*1024*1024); 

    allocmem<<>>(); 

    usemem<<>>();

    usemem<<>>();

    usemem<<>>(); 

    freemem<<>>(); 

    cudaDeviceSynchronize();

    system("pause");

    return 0;

   

}