stackoom
  简体   繁体   中英

CUDA: Filling a column-major matrix

 原文  2017-07-30 09:05:39  4   2    c++/ cuda

Question

I am fairly new to CUDA, and I am trying to offload to the GPU some cumbersome computations I am doing for a performance-critical project. On my computer I have two NVS 510 Graphic cards, but I am currently experimenting with one only.

I have some big column-major matrix (1000-5000 rows x 1-5 M columns) to be filled. I was so far able to write the code to fill the matrix like it were an array, and it works well for matrices of relatively small size. 

__global__ void interp_kernel(fl_type * d_matrix, fl_type* weights, [other params], 
int n_rows, int num_cols) {
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   int column = index / n_rows;
   int row = index % n_rows;
   if (row > n_sim || column > num_cols) return;
   d_matrix[index] = …something(row, column,[other params]);
}

The kernel is called: 

fl_type *res;
cudaMalloc((void**)&res, n_columns*n_rows*fl_size);
int block_size = 1024;
int num_blocks = (n_rows* n_columns + block_size - 1) / block_size;
std::cout << "num_blocks:" << num_blocks << std::endl;
interp_kernel << < num_blocks, block_size >> > (res,[other params], n_rows,n_columns);

and everything works just fine. If I change the kernel to work with 2D threads: 

__global__ void interp_kernel2D(fl_type * d_matrix, fl_type* weights, [other params], 
int n_rows, int num_cols) {
int column = blockIdx.x * blockDim.x + threadIdx.x;
int row = blockIdx.y * blockDim.y + threadIdx.y;
int index = column* n_rows + row;
if (row > n_rows || column > num_cols) return;
   d_matrix[index] = …something(row, column,[other params]);
}

and I invoke it 

int block_size2 = 32; //each block will have block_size2*block_size2 threads
dim3 num_blocks2(block_size2, block_size2);
int x_grid = (n_columns + block_size2 - 1) / block_size2;
int y_grid = (n_rows + block_size2 - 1) / block_size2;
dim3 grid_size2(x_grid, y_grid);
interp_kernel2D <<< grid_size2, num_blocks2 >>> (res,[other params], n_rows,n_columns);

the results are all zero and CUDA returns unknown error. What am I missing? the actual code, which compiles without error with VS2015 and CUDA 8.0, can be found here: https://pastebin.com/XBCVC7VV

Here is the code from the pastebin link: 

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <assert.h>
#include <iostream>
#include <random>
#include <chrono>
typedef float fl_type;
typedef int pos_type;
typedef std::chrono::milliseconds ms;
//declaration of the cuda function
void cuda_interpolation_function(fl_type* interp_value_back, int result_size, fl_type * grid_values, int grid_values_size, fl_type* weights, pos_type* node_map, int  total_action_number, int  interp_dim, int n_sim);

fl_type iterp_cpu(fl_type* weights, pos_type* node_map, fl_type* grid_values, int& row, int& column, int& interp_dim, int& n_sim) {
    int w_p = column*interp_dim;
    fl_type res = weights[w_p] * grid_values[row + node_map[w_p] * n_sim];
    for (int inter_point = 1; inter_point < interp_dim; inter_point++) {
        res += weights[w_p + inter_point] * grid_values[node_map[w_p + inter_point] * n_sim + row];
    }
    return res;
}


__global__ void interp_kernel(fl_type * d_matrix, fl_type* weights, pos_type* node_map, fl_type* grid_values, int interp_dim, int n_sim, int num_cols) {
    int index = blockIdx.x * blockDim.x + threadIdx.x;
    int column = index / n_sim;
    int row = index % n_sim;
    int w_p = column*interp_dim;
    if (row > n_sim || column > num_cols) return;
    fl_type res = weights[w_p] * grid_values[row + node_map[w_p] * n_sim];
    for (int inter_point = 1; inter_point < interp_dim; inter_point++) {
        res += weights[w_p + inter_point] * grid_values[row + node_map[w_p + inter_point] * n_sim];
    }
    d_matrix[index] = res;
}

__global__ void interp_kernel2D(fl_type * d_matrix, fl_type* weights, pos_type* node_map, fl_type* grid_values, int interp_dim, int n_sim, int num_cols) {
    int column = blockIdx.x * blockDim.x + threadIdx.x;
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int index = column*n_sim + row;
    int w_p = column*interp_dim;
    if (row > n_sim || column > num_cols) return;
    fl_type res = weights[w_p] * grid_values[row + node_map[w_p] * n_sim];
    for (int inter_point = 1; inter_point < interp_dim; inter_point++) {
        res += weights[w_p + inter_point] * grid_values[row + node_map[w_p + inter_point] * n_sim];
    }
    d_matrix[index] = res;
}

void verify(fl_type *host, fl_type *device, int size) {
    int count = 0;
    int count_zero = 0;
    for (int i = 0; i < size; i++) {
        if (host[i] != device[i]) {
            count++;
            //std::cout <<"pos: " <<i<< " CPU:" <<h[i] << ",        GPU: " << d[i] <<std::endl;
            assert(host[i] == device[i]);
            if (device[i] == 0.0)
                count_zero++;
        }
    }
    if (count) {
        std::cout << "Non matching: " << count << "out of " << size << "(" << (float(count) / size * 100) << "%)" << std::endl;
        std::cout << "Zeros returned from the device: " << count_zero <<"(" << (float(count_zero) / size * 100) << "%)" << std::endl;
    }
    else
        std::cout << "Perfect match!" << std::endl;
}

int main() {
    int fl_size = sizeof(fl_type);
    int pos_size = sizeof(pos_type);
    int dim = 5;             // range: 2-5
    int number_nodes = 5500; // range: 10.000-500.000
    int max_actions = 12;    // range: 6-200
    int n_sim = 1000;        // range: 1.000-10.000
    int interp_dim = std::pow(2, dim);
    int grid_values_size = n_sim*number_nodes;
    std::default_random_engine generator;
    std::normal_distribution<fl_type> normal_dist(0.0, 1);
    std::uniform_int_distribution<> uniform_dist(0, number_nodes - 1);

    double bit_allocated = 0;
    fl_type * grid_values;  //flattened 2d array, containing the value of the grid (n_sims x number_nodes)
    grid_values = (fl_type *)malloc(grid_values_size * fl_size);
    bit_allocated += grid_values_size * fl_size;
    for (int i = 0; i < grid_values_size; i++)
        grid_values[i] = normal_dist(generator);

    pos_type * map_node2values_start; //vector that maps each node to the first column of the result matrix regarding that done
    pos_type * map_node2values_how_many; //vector that stores how many action we have per node  
    map_node2values_start = (pos_type *)malloc(number_nodes * pos_size);
    map_node2values_how_many = (pos_type *)malloc(number_nodes * pos_size);


    bit_allocated += 2 * (number_nodes * pos_size);
    for (int i = 0; i < number_nodes; i++) {
        //each node as simply max_actions
        map_node2values_start[i] = max_actions*i;
        map_node2values_how_many[i] = max_actions;
    }

    //total number of actions, which is amount of column of the results
    int total_action_number = map_node2values_start[number_nodes - 1] + map_node2values_how_many[number_nodes - 1];

    //vector that keep tracks of the columnt to grab, and their weight in the interpolation
    fl_type* weights;
    pos_type * node_map;
    weights = (fl_type *)malloc(total_action_number*interp_dim * pos_size);
    bit_allocated += total_action_number * fl_size;
    node_map = (pos_type *)malloc(total_action_number*interp_dim * pos_size);
    bit_allocated += total_action_number * pos_size;

    //filling with random numbers
    for (int i = 0; i < total_action_number*interp_dim; i++) {
        node_map[i] = uniform_dist(generator);      // picking random column
        weights[i] = 1.0 / interp_dim;              // uniform weights
    }
    std::cout << "done filling!" << std::endl;
    std::cout << bit_allocated / 8 / 1024 / 1024 << "MB allocated" << std::endl;

    int result_size = n_sim*total_action_number;
    fl_type *interp_value_cpu;
    bit_allocated += result_size* fl_size;



    interp_value_cpu = (fl_type *)malloc(result_size* fl_size);

    auto start = std::chrono::steady_clock::now();
    for (int row = 0; row < n_sim; row++) {
        for (int column = 0; column < total_action_number; column++) {
            auto zz = iterp_cpu(weights, node_map, grid_values, row, column, interp_dim, n_sim);
            interp_value_cpu[column*n_sim + row] = zz;
        }
    }
    auto elapsed_cpu = std::chrono::steady_clock::now() - start;
    std::cout << "Crunching values on the CPU (serial): " << std::chrono::duration_cast<ms>(elapsed_cpu).count() / 1000.0 << "s" << std::endl;
    int * pp;
    cudaMalloc((void**)&pp, sizeof(int)); //initializing the device, to not affect the benchmark
    fl_type *interp_value_gpu;
    interp_value_gpu = (fl_type *)malloc(result_size* fl_size);
    start = std::chrono::steady_clock::now();
    cuda_interpolation_function(interp_value_gpu, result_size, grid_values, grid_values_size, weights, node_map, total_action_number, interp_dim, n_sim);
    auto elapsed_gpu = std::chrono::steady_clock::now() - start;
    std::cout << "Crunching values on the GPU: " << std::chrono::duration_cast<ms>(elapsed_gpu).count() / 1000.0 << "s" << std::endl;
    float ms_cpu = std::chrono::duration_cast<ms>(elapsed_cpu).count();
    float ms_gpu = std::chrono::duration_cast<ms>(elapsed_gpu).count();
    int n_proc = 4;
    std::cout << "Performance: " << (ms_gpu- ms_cpu / n_proc) / (ms_cpu / n_proc) * 100 << " % less time than parallel CPU!" << std::endl;
    verify(interp_value_cpu, interp_value_gpu, result_size);

    free(interp_value_cpu);
    free(interp_value_gpu);
    free(grid_values);
    free(node_map);
    free(weights);
}

void cuda_interpolation_function(fl_type* interp_value_gpu, int result_size, fl_type * grid_values, int grid_values_size, fl_type* weights, pos_type* node_map, int total_action_number, int interp_dim, int n_sim) {
    int fl_size = sizeof(fl_type);
    int pos_size = sizeof(pos_type);
    auto start = std::chrono::steady_clock::now();
    //device versions of the inputs
    fl_type * grid_values_device;
    fl_type* weights_device;
    pos_type * node_map_device;
    fl_type *interp_value_device;
    int lenght_node_map = interp_dim*total_action_number;
    std::cout << "size grid_values: " << grid_values_size <<std::endl;
    std::cout << "size weights: " << lenght_node_map << std::endl;
    std::cout << "size interp_value: " << result_size << std::endl;

    //allocating and moving to the GPU the inputs
    auto error_code=cudaMalloc((void**)&grid_values_device, grid_values_size*fl_size);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMalloc of the grid_values" << std::endl;
    }
    error_code=cudaMemcpy(grid_values_device, grid_values, grid_values_size*fl_size, cudaMemcpyHostToDevice);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMemcpy of the grid_values" << std::endl;
    }
    error_code=cudaMalloc((void**)&weights_device, lenght_node_map*fl_size);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMalloc of the weights" << std::endl;
    }
    error_code=cudaMemcpy(weights_device, weights, lenght_node_map*fl_size, cudaMemcpyHostToDevice);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMemcpy of the weights" << std::endl;
    }
    error_code=cudaMalloc((void**)&node_map_device, lenght_node_map*pos_size);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMalloc of node_map" << std::endl;
    }
    error_code=cudaMemcpy(node_map_device, node_map, lenght_node_map*pos_size, cudaMemcpyHostToDevice);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMemcpy of node_map" << std::endl;
    }
    error_code=cudaMalloc((void**)&interp_value_device, result_size*fl_size);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMalloc of interp_value_device " << std::endl;
    }
    auto elapsed_moving = std::chrono::steady_clock::now() - start;
    float ms_moving = std::chrono::duration_cast<ms>(elapsed_moving).count();
    cudaDeviceSynchronize();
    //1d
    int block_size = 1024;
    int num_blocks = (result_size + block_size - 1) / block_size;
    std::cout << "num_blocks:" << num_blocks << std::endl;
    interp_kernel << < num_blocks, block_size >> > (interp_value_device, weights_device, node_map_device, grid_values_device, interp_dim, n_sim, total_action_number);


    //2d
    //int block_size2 = 32; //each block will have block_size2*block_size2 threads
    //dim3 num_blocks2(block_size2, block_size2);
    //int x_grid = (total_action_number + block_size2 - 1) / block_size2;
    //int y_grid = (n_sim + block_size2 - 1) / block_size2;
    //dim3 grid_size2(x_grid, y_grid);
    //std::cout <<"grid:"<< x_grid<<" x "<< y_grid<<std::endl;
    //interp_kernel2D <<< grid_size2, num_blocks2 >>> (interp_value_device, weights_device, node_map_device, grid_values_device, interp_dim, n_sim, total_action_number);


    cudaDeviceSynchronize();
    cudaError err = cudaGetLastError();
    if (cudaSuccess != err)
    {
        std::cout << "Cuda kernel failed! " << cudaGetErrorString(err) <<std::endl;
    }
    start = std::chrono::steady_clock::now();
    cudaMemcpy(interp_value_gpu, interp_value_device, result_size*fl_size, cudaMemcpyDeviceToHost);
    auto elapsed_moving_back = std::chrono::steady_clock::now() - start;
    float ms_moving_back = std::chrono::duration_cast<ms>(elapsed_moving_back).count();

    std::cout << "Time spent moving the data to the GPU:" << ms_moving << " ms"<<std::endl;
    std::cout << "Time spent moving the results back to the host: " << ms_moving_back << " ms" << std::endl;

    cudaFree(interp_value_device);
    cudaFree(weights_device);
    cudaFree(node_map_device);
    cudaFree(grid_values_device);
}

Moreover, I would extremely grateful for any direction on how to improve the performance of the code.

2 answers

solution1
 1 ACCPTED  2017-07-30 14:55:41

Any time you are having trouble with a CUDA code, I recommend doing proper CUDA error checking (which you mostly seem to be doing), and also run your code with cuda-memcheck . This last utility is similar to "enabling the memory checker" in Nsight VSE, but not quite the same. However the Nsight VSE memory checker may have given you the same indication.

In C (or C++) indexing of arrays generally starts at 0. Therefore, to test for an out-of-bounds index, I must check to see if the generated index is equal to or greater than the size of the array. But in your case you are only testing for greater than: 

if (row > n_sim || column > num_cols) return;

You make a similar error in both your 1D kernel and in your 2D kernel, and although you believe your 1D kernel is working correctly, it is actually making out-of-bounds accesses. You can verify this if you run with the aforementioned cuda-memcheck utility (or probably also with the memory checker that can be enabled in Nsight VSE).

When I modify your code in the pastebin link to use proper range/bounds checking, cuda-memcheck reports no errors, and your program reports the correct results. I've tested both cases, but the code below is modified from your pastebin link to uncomment the 2D case, and use that instead of the 1D case: 

$ cat t375.cu | more
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <assert.h>
#include <iostream>
#include <random>
#include <chrono>
typedef float fl_type;
typedef int pos_type;
typedef std::chrono::milliseconds ms;
//declaration of the cuda function
void cuda_interpolation_function(fl_type* interp_value_back, int result_size, fl
_type * grid_values, int grid_values_size, fl_type* weights, pos_type* node_map,
 int  total_action_number, int  interp_dim, int n_sim);

fl_type iterp_cpu(fl_type* weights, pos_type* node_map, fl_type* grid_values, in
t& row, int& column, int& interp_dim, int& n_sim) {
    int w_p = column*interp_dim;
    fl_type res = weights[w_p] * grid_values[row + node_map[w_p] * n_sim];
    for (int inter_point = 1; inter_point < interp_dim; inter_point++) {
        res += weights[w_p + inter_point] * grid_values[node_map[w_p + inter_poi
nt] * n_sim + row];
    }
    return res;
}


__global__ void interp_kernel(fl_type * d_matrix, fl_type* weights, pos_type* no
de_map, fl_type* grid_values, int interp_dim, int n_sim, int num_cols) {
    int index = blockIdx.x * blockDim.x + threadIdx.x;
    int column = index / n_sim;
    int row = index % n_sim;
    int w_p = column*interp_dim;
    if (row >= n_sim || column >= num_cols) return;  // modified
    fl_type res = weights[w_p] * grid_values[row + node_map[w_p] * n_sim];
    for (int inter_point = 1; inter_point < interp_dim; inter_point++) {
        res += weights[w_p + inter_point] * grid_values[row + node_map[w_p + int
er_point] * n_sim];
    }
    d_matrix[index] = res;
}

__global__ void interp_kernel2D(fl_type * d_matrix, fl_type* weights, pos_type*
node_map, fl_type* grid_values, int interp_dim, int n_sim, int num_cols) {
    int column = blockIdx.x * blockDim.x + threadIdx.x;
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int index = column*n_sim + row;
    int w_p = column*interp_dim;
    if (row >= n_sim || column >= num_cols) return;  // modified
    fl_type res = weights[w_p] * grid_values[row + node_map[w_p] * n_sim];
    for (int inter_point = 1; inter_point < interp_dim; inter_point++) {
        res += weights[w_p + inter_point] * grid_values[row + node_map[w_p + int
er_point] * n_sim];
    }
    d_matrix[index] = res;
}

void verify(fl_type *host, fl_type *device, int size) {
    int count = 0;
    int count_zero = 0;
    for (int i = 0; i < size; i++) {
        if (host[i] != device[i]) {
            count++;
            //std::cout <<"pos: " <<i<< " CPU:" <<h[i] << ",        GPU: " << d[
i] <<std::endl;
            assert(host[i] == device[i]);
            if (device[i] == 0.0)
                count_zero++;
        }
    }
    if (count) {
        std::cout << "Non matching: " << count << "out of " << size << "(" << (f
loat(count) / size * 100) << "%)" << std::endl;
        std::cout << "Zeros returned from the device: " << count_zero <<"(" << (
float(count_zero) / size * 100) << "%)" << std::endl;
    }
    else
        std::cout << "Perfect match!" << std::endl;
}

int main() {
    int fl_size = sizeof(fl_type);
    int pos_size = sizeof(pos_type);
    int dim = 5;             // range: 2-5
    int number_nodes = 5500; // range: 10.000-500.000
    int max_actions = 12;    // range: 6-200
    int n_sim = 1000;        // range: 1.000-10.000
    int interp_dim = std::pow(2, dim);
    int grid_values_size = n_sim*number_nodes;
    std::default_random_engine generator;
    std::normal_distribution<fl_type> normal_dist(0.0, 1);
    std::uniform_int_distribution<> uniform_dist(0, number_nodes - 1);

    double bit_allocated = 0;
    fl_type * grid_values;  //flattened 2d array, containing the value of the grid (n_sims x number_nodes)
    grid_values = (fl_type *)malloc(grid_values_size * fl_size);
    bit_allocated += grid_values_size * fl_size;
    for (int i = 0; i < grid_values_size; i++)
        grid_values[i] = normal_dist(generator);

    pos_type * map_node2values_start; //vector that maps each node to the first column of the result matrix regarding that done
    pos_type * map_node2values_how_many; //vector that stores how many action we have per node
    map_node2values_start = (pos_type *)malloc(number_nodes * pos_size);
    map_node2values_how_many = (pos_type *)malloc(number_nodes * pos_size);


    bit_allocated += 2 * (number_nodes * pos_size);
    for (int i = 0; i < number_nodes; i++) {
        //each node as simply max_actions
        map_node2values_start[i] = max_actions*i;
        map_node2values_how_many[i] = max_actions;
    }

    //total number of actions, which is amount of column of the results
    int total_action_number = map_node2values_start[number_nodes - 1] + map_node2values_how_many[number_nodes - 1];

    //vector that keep tracks of the columnt to grab, and their weight in the interpolation
    fl_type* weights;
    pos_type * node_map;
    weights = (fl_type *)malloc(total_action_number*interp_dim * pos_size);
    bit_allocated += total_action_number * fl_size;
    node_map = (pos_type *)malloc(total_action_number*interp_dim * pos_size);
    bit_allocated += total_action_number * pos_size;

    //filling with random numbers
    for (int i = 0; i < total_action_number*interp_dim; i++) {
        node_map[i] = uniform_dist(generator);      // picking random column
        weights[i] = 1.0 / interp_dim;              // uniform weights
    }
    std::cout << "done filling!" << std::endl;
    std::cout << bit_allocated / 8 / 1024 / 1024 << "MB allocated" << std::endl;

    int result_size = n_sim*total_action_number;
    fl_type *interp_value_cpu;
    bit_allocated += result_size* fl_size;



    interp_value_cpu = (fl_type *)malloc(result_size* fl_size);

    auto start = std::chrono::steady_clock::now();
    for (int row = 0; row < n_sim; row++) {
        for (int column = 0; column < total_action_number; column++) {
            auto zz = iterp_cpu(weights, node_map, grid_values, row, column, interp_dim, n_sim);
            interp_value_cpu[column*n_sim + row] = zz;
        }
    }
    auto elapsed_cpu = std::chrono::steady_clock::now() - start;
    std::cout << "Crunching values on the CPU (serial): " << std::chrono::duration_cast<ms>(elapsed_cpu).count() / 1000.0 << "s" << std::endl;
    int * pp;
    cudaMalloc((void**)&pp, sizeof(int)); //initializing the device, to not affect the benchmark
    fl_type *interp_value_gpu;
    interp_value_gpu = (fl_type *)malloc(result_size* fl_size);
    start = std::chrono::steady_clock::now();
    cuda_interpolation_function(interp_value_gpu, result_size, grid_values, grid_values_size, weights, node_map, total_action_number, interp_dim, n_sim);
    auto elapsed_gpu = std::chrono::steady_clock::now() - start;
    std::cout << "Crunching values on the GPU: " << std::chrono::duration_cast<ms>(elapsed_gpu).count() / 1000.0 << "s" << std::endl;
    float ms_cpu = std::chrono::duration_cast<ms>(elapsed_cpu).count();
    float ms_gpu = std::chrono::duration_cast<ms>(elapsed_gpu).count();
    int n_proc = 4;
    std::cout << "Performance: " << (ms_gpu- ms_cpu / n_proc) / (ms_cpu / n_proc) * 100 << " % less time than parallel CPU!" << std::endl;
    verify(interp_value_cpu, interp_value_gpu, result_size);

    free(interp_value_cpu);
    free(interp_value_gpu);
    free(grid_values);
    free(node_map);
    free(weights);
}

void cuda_interpolation_function(fl_type* interp_value_gpu, int result_size, fl_type * grid_values, int grid_values_size, fl_type* weights, pos_type* node_map, int total_action_number, int interp_dim, int n_sim) {
    int fl_size = sizeof(fl_type);
    int pos_size = sizeof(pos_type);
    auto start = std::chrono::steady_clock::now();
    //device versions of the inputs
    fl_type * grid_values_device;
    fl_type* weights_device;
    pos_type * node_map_device;
    fl_type *interp_value_device;
    int lenght_node_map = interp_dim*total_action_number;
    std::cout << "size grid_values: " << grid_values_size <<std::endl;
    std::cout << "size weights: " << lenght_node_map << std::endl;
    std::cout << "size interp_value: " << result_size << std::endl;

    //allocating and moving to the GPU the inputs
    auto error_code=cudaMalloc((void**)&grid_values_device, grid_values_size*fl_size);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMalloc of the grid_values" << std::endl;
    }
    error_code=cudaMemcpy(grid_values_device, grid_values, grid_values_size*fl_size, cudaMemcpyHostToDevice);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMemcpy of the grid_values" << std::endl;
    }
    error_code=cudaMalloc((void**)&weights_device, lenght_node_map*fl_size);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMalloc of the weights" << std::endl;
    }
    error_code=cudaMemcpy(weights_device, weights, lenght_node_map*fl_size, cudaMemcpyHostToDevice);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMemcpy of the weights" << std::endl;
    }
    error_code=cudaMalloc((void**)&node_map_device, lenght_node_map*pos_size);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMalloc of node_map" << std::endl;
    }
    error_code=cudaMemcpy(node_map_device, node_map, lenght_node_map*pos_size, cudaMemcpyHostToDevice);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMemcpy of node_map" << std::endl;
    }
    error_code=cudaMalloc((void**)&interp_value_device, result_size*fl_size);
    if (error_code != cudaSuccess) {
        std::cout << "Error during cudaMalloc of interp_value_device " << std::endl;
    }
    auto elapsed_moving = std::chrono::steady_clock::now() - start;
    float ms_moving = std::chrono::duration_cast<ms>(elapsed_moving).count();
    cudaDeviceSynchronize();
    //1d
#if 0
    int block_size = 1024;
    int num_blocks = (result_size + block_size - 1) / block_size;
    std::cout << "num_blocks:" << num_blocks << std::endl;
    interp_kernel << < num_blocks, block_size >> > (interp_value_device, weights_device, node_map_device, grid_values_device, interp_dim, n_sim, total_action_number);
#endif

    //2d
    int block_size2 = 32; //each block will have block_size2*block_size2 threads
    dim3 num_blocks2(block_size2, block_size2);
    int x_grid = (total_action_number + block_size2 - 1) / block_size2;
    int y_grid = (n_sim + block_size2 - 1) / block_size2;
    dim3 grid_size2(x_grid, y_grid);
    std::cout <<"grid:"<< x_grid<<" x "<< y_grid<<std::endl;
    interp_kernel2D <<< grid_size2, num_blocks2 >>> (interp_value_device, weights_device, node_map_device, grid_values_device, interp_dim, n_sim, total_action_number);


    cudaDeviceSynchronize();
    cudaError err = cudaGetLastError();
    if (cudaSuccess != err)
    {
        std::cout << "Cuda kernel failed! " << cudaGetErrorString(err) <<std::endl;
    }
    start = std::chrono::steady_clock::now();
    cudaMemcpy(interp_value_gpu, interp_value_device, result_size*fl_size, cudaMemcpyDeviceToHost);
    auto elapsed_moving_back = std::chrono::steady_clock::now() - start;
    float ms_moving_back = std::chrono::duration_cast<ms>(elapsed_moving_back).count();

    std::cout << "Time spent moving the data to the GPU:" << ms_moving << " ms"<<std::endl;
    std::cout << "Time spent moving the results back to the host: " << ms_moving_back << " ms" << std::endl;

    cudaFree(interp_value_device);
    cudaFree(weights_device);
    cudaFree(node_map_device);
    cudaFree(grid_values_device);
}
$ nvcc -arch=sm_52 -o t375 t375.cu -std=c++11
$ cuda-memcheck ./t375
========= CUDA-MEMCHECK
done filling!
2.69079MB allocated
Crunching values on the CPU (serial): 30.081s
size grid_values: 5500000
size weights: 2112000
size interp_value: 66000000
grid:2063 x 32
Time spent moving the data to the GPU:31 ms
Time spent moving the results back to the host: 335 ms
Crunching values on the GPU: 7.089s
Performance: -5.73452 % less time than parallel CPU!
Perfect match!
========= ERROR SUMMARY: 0 errors
$

Note that cuda-memcheck slows down the execution of your program on the GPU to do rigorous memory bounds checking. Therefore the performance may not match the ordinary case. This is what an "ordinary" run looks like: 

$ ./t375
done filling!
2.69079MB allocated
Crunching values on the CPU (serial): 30.273s
size grid_values: 5500000
size weights: 2112000
size interp_value: 66000000
grid:2063 x 32
Time spent moving the data to the GPU:32 ms
Time spent moving the results back to the host: 332 ms
Crunching values on the GPU: 1.161s
Performance: -84.6596 % less time than parallel CPU!
Perfect match!
$

solution2
 1  

You are accessing memory beyond the allocated chunk. To check if row and column indices are within the range: 

if (row >= n_rows || column >= num_cols) return;      // Do this
if (row >  n_rows || column >  num_cols) return;      // Instead of this

In flat version this int row = index % n_rows; makes row stay below the n_rows . You only access one column beyond the allocated memory, which for small matrix could still be withing the memory alignment. Python demo .

The second version does access an extra column plus and extra element, and one extra element for each row (the first element of the following row), as this: 

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

no longer keeps row index within the valid range. Python demo .

Looking at your pastebin, this is probably the place where it breaks: 

44.   fl_type res = weights[w_p] * grid_values[row + node_map[w_p] * n_sim];

                                               ^^^

45.   for (int inter_point = 1; inter_point < interp_dim; inter_point++) {
46.       res += weights[w_p + inter_point] * \
           grid_values[row + node_map[w_p + inter_point] * n_sim];

                       ^^^
47.   }

暂无
暂无

The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.

Related Question row-major or column-major access of thread index in cuda? Confusion between C++ and OpenGL matrix order (row-major vs column-major) sparse matrix Coordinate Storage format: convert from row-major to column-major convert from C array (column-major) to armadillo matrix (arma::mat) without copying Accessing Class / Union Data in “Column-Major” Format C++ Why Eigen use Column-Major by default instead of Row-Major? matrix multiplication as column major Eigen library got “comma initilizer”, why make column-major the default? GLM - Matrix from orientation is creating row major and not column major matrix? How to check if Eigen Matrix is column major or row major?
Related Tags
 
粤ICP备18138465号  © 2020-2024 STACKOOM.COM