GPU Tips

<1> Basic

#include <stdio.h>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#define NUM 15
__global__ void square(float *dout,float *din)
{
    int idx = threadIdx.x;
    float f  = din[idx];
    dout[idx] = f*f;
}


int main(int argc,char **argv)
{
        
    const int bytes = sizeof(float) * NUM;
    float host_in[NUM];
    // save some value
    for(int i=0;i<NUM;i++)
    {
        host_in[i] = float(i);
    }

    float host_out[NUM];

    cudaError_t cudaStatus;
    // GPU SETTINGS
    // Choose which GPU to run on, change this on a multi-GPU system.
    cudaStatus = cudaSetDevice(0);
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaSetDevice failed!  Do you have a CUDA-capable GPU installed?");
        return;
    }





    // define gpu memory, GPU memory allocation
    float *device_in =  0;
    float *device_out = 0;
    cudaStatus = cudaMalloc((void**)&device_in, bytes);
    cudaStatus = cudaMalloc((void**)&device_out,bytes);

    cudaStatus = cudaMemcpy(device_in,host_in,bytes,cudaMemcpyHostToDevice);



    // GPU kernel
    // 1 block,Num threads
    square<<<1,NUM>>>(device_out,device_in);

    cudaStatus = cudaDeviceSynchronize();
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!
", cudaStatus);
    }


    cudaStatus = cudaMemcpy(host_out, device_out, bytes, cudaMemcpyDeviceToHost);
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaMemcpy failed!");
    }


    // Free GPU memory
    cudaFree(device_in);
    cudaFree(device_out);

    for(int i=0;i<NUM;i++)
    {
        fprintf(stdout,"%f 
",host_out[i]);
    }

    getchar();

    return 0;

}

 <2> N blocks and block's threads one dim

#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <stdio.h>
#include <stdlib.h>
#define ARRAYSize 50000000
#define THREADS_PER_BLOCK 1024

#define fnvalue(a,size)
{
    for(int i=0;i<size;i++)    
    {
       a[i] = float(i);
    }
}

#define CHECK_CUDA_STATUS(STATUS)
{
    if (STATUS != cudaSuccess)
    {
        fprintf(stdout,"Error in line %d
 ",__LINE__);
    }
}



__global__ void add(float *d_out,float *d_x, float *d_y)
{
    
    int index = blockIdx.x * blockDim.x + threadIdx.x;
    if (index<ARRAYSize)
    {
        d_out[index] = d_x[index] + d_y[index];
    }
    
}

int main(int argc,char **argv)
{

    const int bytes = sizeof(float)*ARRAYSize;

    // host memory
    float *h_x   = (float*)malloc(bytes);
    float *h_y   = (float*)malloc(bytes);
    float *h_out = (float*)malloc(bytes);

    // give host value
    fnvalue(h_x,ARRAYSize);
    fnvalue(h_y,ARRAYSize);

    // device memory
    float *d_x,*d_y,*d_out;
    // cuda setttings
    cudaError_t dstat;
    dstat = cudaSetDevice(0);
    CHECK_CUDA_STATUS(dstat);
    dstat = cudaMalloc((void**)&d_x, bytes);
    CHECK_CUDA_STATUS(dstat);
    dstat = cudaMalloc((void**)&d_y, bytes);
    CHECK_CUDA_STATUS(dstat);
    dstat = cudaMalloc((void**)&d_out, bytes);
    CHECK_CUDA_STATUS(dstat);


    fprintf(stdout,"Copy data go GPU
");
    cudaMemcpy(d_x,h_x,bytes,cudaMemcpyHostToDevice);
    cudaMemcpy(d_y,h_y,bytes,cudaMemcpyHostToDevice);


    add<<<ARRAYSize/THREADS_PER_BLOCK,THREADS_PER_BLOCK>>>(d_out,d_x,d_y);


    fprintf(stdout,"Copy GPU data to cpu
");
    dstat = cudaMemcpy(h_out,d_out,bytes,cudaMemcpyDeviceToHost);

    cudaDeviceSynchronize();

    // DEBUG SOME VALUE
    
    for(int i=100600;i<100900;i++)
    {
        if ((i+1)%4==0)
        {
            fprintf(stdout,"%f
", h_out[i]);
        }
        else
        {
            fprintf(stdout,"%f ", h_out[i]);
        }
    }

    getchar();



    // FREE CPU MEMORY
    free(h_x);
    free(h_y);
    free(h_out);

    // FREE GPU MEMORY
    dstat = cudaFree(d_x);
    CHECK_CUDA_STATUS(dstat);
    dstat = cudaFree(d_y);
    CHECK_CUDA_STATUS(dstat);
    dstat = cudaFree(d_out);
    CHECK_CUDA_STATUS(dstat);

    return 0;
    

}

<3> Unified memory:

#include <iostream>
#include <math.h>
// Kernel function to add the elements of two arrays
__global__
void add(int n, float *x, float *y)
{
  for (int i = 0; i < n; i++)
    y[i] = x[i] + y[i];
}

int main(void)
{
  int N = 1<<20;
  float *x, *y;

  // Allocate Unified Memory – accessible from CPU or GPU
  cudaMallocManaged(&x, N*sizeof(float));
  cudaMallocManaged(&y, N*sizeof(float));

  // initialize x and y arrays on the host
  for (int i = 0; i < N; i++) {
    x[i] = 1.0f;
    y[i] = 2.0f;
  }

  // Run kernel on 1M elements on the GPU
  add<<<1, 1>>>(N, x, y);

  // Wait for GPU to finish before accessing on host
  cudaDeviceSynchronize();

  // Check for errors (all values should be 3.0f)
  float maxError = 0.0f;
  for (int i = 0; i < N; i++)
    maxError = fmax(maxError, fabs(y[i]-3.0f));
  std::cout << "Max error: " << maxError << std::endl;

  // Free memory
  cudaFree(x);
  cudaFree(y);
  
  return 0;
}

<4>Some tips

(1)

下图表示一维的block是由grid生成的。

__global__
void add(int n, float *x, float *y)
{
  int index = blockIdx.x * blockDim.x + threadIdx.x;
  int stride = blockDim.x * gridDim.x;
  for (int i = index; i < n; i += stride)
    y[i] = x[i] + y[i];
}

(2)  关于SharedMemory ,其实是在一个block上的共享memory

 

 code:

#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <device_functions.h>

#define RADIUS 3
#define BLOCKSIZE 10

__global__ void process(int *d_out,int *d_in,int *shared_mem)
{
    __shared__ int temp[BLOCKSIZE + 2* RADIUS ];
    int gindex = threadIdx.x + blockIdx.x * blockDim.x;
    int lindex = threadIdx.x + RADIUS;
    //printf("%d ",lindex);
    // Read input elements into shared memory
    temp[lindex] = d_in[gindex];


    
    
    if (threadIdx.x < RADIUS) 
    {
        temp[lindex - RADIUS] = d_in[gindex - RADIUS];
        temp[lindex + BLOCKSIZE] = d_in[gindex + BLOCKSIZE];

    }


    shared_mem[lindex] = lindex;
    // this code for debug
    
    


    __syncthreads();
    

    // Apply the stencil
     int result = 0;
     for (int offset = -RADIUS ; offset <= RADIUS ; offset++)
     {
         result += temp[lindex + offset];
         
     }
        
     // Store the result
     d_out[gindex] = result;
     
}



int main(int argc,char**argv)
{
    // allocation of memory
    
    int host_rawSize = 10;

    int host_bytes = sizeof(int) * host_rawSize;
    int shared_bytes = (host_rawSize+2*RADIUS) * sizeof(int);


    int *host_data          = (int*)malloc(host_bytes);
    int *host_outData       = (int*)malloc(host_bytes);
    int *host_sharedMemData = (int*)malloc(shared_bytes);
    for(int i=0;i<host_rawSize;i++)
    {
        host_data[i] = int(i)+1;
    }
    for(int i=0;i<host_rawSize;i++)
    {
        fprintf(stdout,"%d   ",host_data[i]);
    }
    fprintf(stdout,"
");



    int *dev_in;
    cudaMallocManaged((void**)&dev_in  , host_bytes);
    //cudaMallocManaged(&dev_in  , host_bytes);
    //cudaMalloc((void**)&dev_rawdata,bytes);
    cudaMemcpy(dev_in,host_data,host_bytes,cudaMemcpyHostToDevice);



    int dev_out_bytes  = host_rawSize *sizeof(int);  // 4*sizeof(float)
    int *dev_out;
    int *dev_shared;
    cudaMallocManaged(&dev_out    , dev_out_bytes);
    cudaMallocManaged(&dev_shared , shared_bytes);

    process<<<1,host_rawSize>>>(dev_out,dev_in,dev_shared);

    cudaMemcpy(host_outData,      dev_out,   dev_out_bytes,cudaMemcpyDeviceToHost);
    cudaMemcpy(host_sharedMemData,dev_shared,shared_bytes,cudaMemcpyDeviceToHost);


    printf("===============Debug the gpu shared memory=======================
");
    for(int i=0;i<host_rawSize + 2*RADIUS;i++)
    {
        fprintf(stdout,"%d   ",host_sharedMemData[i]);
    }
    printf("
===============Debug the gpu shared memory=======================
");


    for(int i=0;i<host_rawSize;i++)
    {
        fprintf(stdout,"%d   ",host_outData[i]);
    }
    fprintf(stdout,"
");

    getchar();

    return 0;
}

<1>simple caculation:
I = (R+G+B)/3

I = R*0.299f + G*0.587f + 0.114f*B

CPU:

// Serial implementation for running on CPU using a single thread.
void rgbaToGreyscaleCpu(const uchar4* const rgbaImage, unsigned char *const greyImage,
        const size_t numRows, const size_t numCols)
{
    for (size_t r = 0; r < numRows; ++r) {
        for (size_t c = 0; c < numCols; ++c) {
            const uchar4 rgba = rgbaImage[r * numCols + c];
            const float channelSum = .299f * rgba.x + .587f * rgba.y + .114f * rgba.z;
            greyImage[r * numCols + c] = channelSum;
        }
    }
}

GPU:

// CUDA kernel which is run in parallel by many GPU threads.
__global__
void rgbaToGreyscaleCudaKernel(const uchar4* const rgbaImage,
        unsigned char* const greyImage,
        const int numRows, const int numCols)
{
    //First create a mapping from the 2D block and grid locations
    //to an absolute 2D location in the image, then use that to
    //calculate a 1D offset
    const long pointIndex = threadIdx.x + blockDim.x*blockIdx.x;
 
    if(pointIndex<numRows*numCols) { // this is necessary only if too many threads are started
        uchar4 const imagePoint = rgbaImage[pointIndex];
        greyImage[pointIndex] = .299f*imagePoint.x + .587f*imagePoint.y  + .114f*imagePoint.z;
    }
}
 
// Parallel implementation for running on GPU using multiple threads.
void rgbaToGreyscaleCuda(const uchar4 * const h_rgbaImage, uchar4 * const d_rgbaImage,
        unsigned char* const d_greyImage, const size_t numRows, const size_t numCols)
{
    const int blockThreadSize = 256;
    const int numberOfBlocks = 1 + ((numRows*numCols - 1) / blockThreadSize); // a/b rounded up
    const dim3 blockSize(blockThreadSize, 1, 1);
    const dim3 gridSize(numberOfBlocks , 1, 1);
    rgbaToGreyscaleCudaKernel<<<gridSize, blockSize>>>(d_rgbaImage, d_greyImage, numRows, numCols);
}