tex2D使用学习

1. 背景:

        项目中使用到了纹理进行插值的加速,因此记录一些自己在学习tex2D的一些过程

2. 代码:

        

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include 
#include 
#include 
#include 
#include 

void Data2Half(half* pDst, const int16_t* pSrc, const int Ndots);
static __global__ void Tex2DTest(cudaTextureObject_t p_rf_data, float* pfRes1, float* pfRes2);


static __global__ void data2half(half* pDst, const int16_t* pSrc, const int Ndots)
{
    const int tid = blockIdx.x * blockDim.x + threadIdx.x;
    if (tid >= Ndots)
        return;

    pDst[tid] = __short2half_rn(pSrc[tid]);
}

cudaTextureObject_t m_tex   = 0;
cudaArray* m_pRFData        = nullptr;
int16_t* m_i16RFDataBuffer  = nullptr; // 设备端的RF数据
half* m_pHalfRFDataCache    = nullptr; // 转换为半浮点型的RF数据缓存,用于将SHORT类型转换为FLOAT类型

int main()
{
    const int nRx     = 2;
    const int Nsample = 2;
    const int IQ      = 1;
    cudaError_t error;

    cudaChannelFormatDesc channelDesc = cudaCreateChannelDescHalf();
    error                             = cudaMallocArray(&m_pRFData, &channelDesc, nRx * IQ, Nsample, cudaArrayTextureGather);
    assert(m_pRFData);

    cudaResourceDesc texRes;
    memset(&texRes, 0, sizeof(cudaResourceDesc));
    texRes.resType         = cudaResourceTypeArray;
    texRes.res.array.array = m_pRFData;

    cudaTextureDesc texDescr;
    memset(&texDescr, 0, sizeof(cudaTextureDesc));
    texDescr.normalizedCoords = false;
    texDescr.filterMode       = cudaFilterModeLinear;  // 这里很重要
    texDescr.addressMode[0]   = cudaAddressModeBorder;
    texDescr.addressMode[1]   = cudaAddressModeBorder;


    error = cudaCreateTextureObject(&m_tex, &texRes, &texDescr, NULL);

    //int16_t pi16Src[nRx * Nsample * IQ] = {1, 11, 2, 22,
    //                                    3, 33, 4, 44, 
    //                                    5, 55, 6, 66, 
    //                                    7, 77, 8, 88};

    //int16_t pi16Src[nRx * Nsample * IQ] = { 1, 11, 2, 22,
    //                                        3, 33, 4, 44};


    int16_t pi16Src[nRx * Nsample * IQ] = { 1,2,
                                           3,4 };

    error = cudaMalloc(&m_i16RFDataBuffer, sizeof(int16_t) * nRx * IQ * Nsample);
    error = cudaMemcpy(m_i16RFDataBuffer, pi16Src, sizeof(int16_t) * nRx * IQ * Nsample, cudaMemcpyHostToDevice);

    error = cudaMalloc(&m_pHalfRFDataCache, sizeof(half) * nRx * IQ * Nsample);
    Data2Half(m_pHalfRFDataCache, m_i16RFDataBuffer, nRx * IQ * Nsample);

    error = cudaMemcpy2DToArray(m_pRFData, 0, 0, m_pHalfRFDataCache, sizeof(half) * nRx * IQ, sizeof(half) * nRx * IQ, Nsample, cudaMemcpyDeviceToDevice);

    float* pf_res1 = nullptr;
    float* pf_res2 = nullptr;

    error = cudaMalloc(&pf_res1, nRx * Nsample * sizeof(float)); cudaMemset(pf_res1, 0, nRx * Nsample * sizeof(float));
    error = cudaMalloc(&pf_res2, nRx * Nsample * sizeof(float)); cudaMemset(pf_res2, 0, nRx * Nsample * sizeof(float));
    
    error = cudaGetLastError();

    dim3 block_dim = dim3(1, 1);
    dim3 grid_dim  = dim3(1, 1);
    Tex2DTest << > > (m_tex, pf_res1, pf_res2);

    cudaDeviceSynchronize();

    std::vector vf_res_1(nRx * Nsample, 0);
    std::vector vf_res_2(nRx * Nsample, 0);

    cudaMemcpy(vf_res_1.data(), pf_res1, sizeof(float) * vf_res_1.size(), cudaMemcpyDeviceToHost);
    cudaMemcpy(vf_res_2.data(), pf_res2, sizeof(float) * vf_res_2.size(), cudaMemcpyDeviceToHost);

    return 0;
}

void Data2Half(half* pDst, const int16_t* pSrc, const int Ndots)
{
    dim3 block = dim3(512, 1);
    dim3 grid = dim3((Ndots - 1) / block.x + 1, 1);
    data2half << < grid, block >> > (pDst, pSrc, Ndots);
}

static __global__ void Tex2DTest(cudaTextureObject_t p_rf_data, float *pfRes1, float *pfRes2)
{

    for (size_t y = 0; y < 2; ++y)
    {
        for (size_t x = 0; x < 2; ++x) 
        {
            float value = tex2D(p_rf_data, x,     y);
            //pfRes1[y * 4 + y] = 

            printf("x: %f\n", value);
        }
    }
}

3. 输出分析:

可以看到执行结果是

tex2D使用学习_第1张图片

为什么呢?

原因是因为tex2D插值导致的,上面测试数据是

1  2

3   4

那在进行插值的时候会变成

0  0   0   0

0   1   2  0

0   3   4  0

每个点的输出都是当前前和左上角3个点进行平均计算出来的

比如第一个输出计算为:(1 + 0 + 0 + 0)/4 = 0.25

最后一个输出的计算为:(1 + 2 + 3 + 4) / 4 = 2.5

4. 问题

        上面只是单独数据实数点的计算,如果我的数据集合是复数怎么办?

        比如一组2 * 2大小的数据对

        (1, 2, 3, 4;

           5,   6, 7, 8)

        数据实际表示含义是

         (1 + j * 2,   3 + j * 4;

            5 + j * 6,   7 + j * 8)

        这种情况下怎么做到正确插值呢,比如第一个实数点的输出结果应该是

         (1 + 0 + 0 + 0)/ 4

           最后一个实数点的输出应该是:

            (1 + 3 + 5 + 7) / 4

           同理,最后一个虚数点的输出应该是:
           (2 + 4 + 6 + 8)/ 4

5. 解决

         

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include 
#include 
#include 
#include 
#include 

void Data2Half(half* pDst, const int16_t* pSrc, const int Ndots);
static __global__ void Tex2DTest(cudaTextureObject_t p_rf_data, float* pfRes1, float* pfRes2);


static __global__ void data2half(half* pDst, const int16_t* pSrc, const int Ndots)
{
    const int tid = blockIdx.x * blockDim.x + threadIdx.x;
    if (tid >= Ndots)
        return;

    pDst[tid] = __short2half_rn(pSrc[tid]);
}

cudaTextureObject_t m_tex = 0;
cudaArray* m_pRFData = nullptr;
int16_t* m_i16RFDataBuffer = nullptr; // 设备端的RF数据
half* m_pHalfRFDataCache = nullptr; // 转换为半浮点型的RF数据缓存,用于将SHORT类型转换为FLOAT类型

using namespace std;

int main()
{
    const int nRx = 2;
    const int Nsample = 2;
    const int IQ = 2;
    cudaError_t error;

    cudaChannelFormatDesc channelDesc = cudaCreateChannelDescHalf2();
    error = cudaMallocArray(&m_pRFData, &channelDesc, nRx, Nsample, cudaArrayTextureGather);
    assert(m_pRFData);

    cudaResourceDesc texRes;
    memset(&texRes, 0, sizeof(cudaResourceDesc));
    texRes.resType = cudaResourceTypeArray;
    texRes.res.array.array = m_pRFData;

    cudaTextureDesc texDescr;
    memset(&texDescr, 0, sizeof(cudaTextureDesc));
    texDescr.normalizedCoords = false;
    texDescr.filterMode = cudaFilterModeLinear;  // 这里很重要
    texDescr.addressMode[0] = cudaAddressModeBorder;
    texDescr.addressMode[1] = cudaAddressModeBorder;


    error = cudaCreateTextureObject(&m_tex, &texRes, &texDescr, NULL);

    //int16_t pi16Src[nRx * Nsample * IQ] = {1, 11, 2, 22,
    //                                    3, 33, 4, 44, 
    //                                    5, 55, 6, 66, 
    //                                    7, 77, 8, 88};

    //int16_t pi16Src[nRx * Nsample * IQ] = { 1, 11, 2, 22,
    //                                        3, 33, 4, 44};


    int16_t pi16Src[nRx * Nsample * IQ] = { 1, 2, 3, 4,
                                            5, 6, 7, 8 };

    error = cudaMalloc(&m_i16RFDataBuffer, sizeof(int16_t) * nRx * IQ * Nsample);
    error = cudaMemcpy(m_i16RFDataBuffer, pi16Src, sizeof(int16_t) * nRx * IQ * Nsample, cudaMemcpyHostToDevice);

    error = cudaMalloc(&m_pHalfRFDataCache, sizeof(half) * nRx * IQ * Nsample);
    Data2Half(m_pHalfRFDataCache, m_i16RFDataBuffer, nRx * IQ * Nsample);

    error = cudaMemcpy2DToArray(m_pRFData, 0, 0, m_pHalfRFDataCache, sizeof(half2) * nRx, sizeof(half2) * nRx, Nsample, cudaMemcpyDeviceToDevice);

    float* pf_res1 = nullptr;
    float* pf_res2 = nullptr;

    error = cudaMalloc(&pf_res1, nRx * Nsample * sizeof(float)); cudaMemset(pf_res1, 0, nRx * Nsample * sizeof(float));
    error = cudaMalloc(&pf_res2, nRx * Nsample * sizeof(float)); cudaMemset(pf_res2, 0, nRx * Nsample * sizeof(float));

    error = cudaGetLastError();

    dim3 block_dim = dim3(1, 1);
    dim3 grid_dim  = dim3(1, 1);
    Tex2DTest << > > (m_tex, pf_res1, pf_res2);

    cudaDeviceSynchronize();

    std::vector vf_res_1(nRx * Nsample, 0);
    std::vector vf_res_2(nRx * Nsample, 0);

    cudaMemcpy(vf_res_1.data(), pf_res1, sizeof(float) * vf_res_1.size(), cudaMemcpyDeviceToHost);
    cudaMemcpy(vf_res_2.data(), pf_res2, sizeof(float) * vf_res_2.size(), cudaMemcpyDeviceToHost);

    return 0;
}

void Data2Half(half* pDst, const int16_t* pSrc, const int Ndots)
{
    dim3 block = dim3(512, 1);
    dim3 grid = dim3((Ndots - 1) / block.x + 1, 1);
    data2half << < grid, block >> > (pDst, pSrc, Ndots);
}

static __global__ void Tex2DTest(cudaTextureObject_t p_rf_data, float* pfRes1, float* pfRes2)
{

    for (size_t y = 0; y < 2; ++y)
    {
        for (size_t x = 0; x < 2; ++x)
        {
            float2 value = tex2D(p_rf_data, x, y);
            //pfRes1[y * 4 + y] = 

            printf("x: %f, y: %f", value.x, value.y);
            // printf("x: %f, y: %f\n", value.x, value.y);
        }
        printf("\n");
    }
}

其实关键是在tex2D的构造

然后按照half2的方式进行排布就好了

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