Explicit FDM with CUDA [closed] - multidimensional-array

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I am working to implement CUDA for the following code. The first version has been written serially and the second version is written with CUDA. I am sure about its results in serial version. I expect that the second version that I have added CUDA functionality also give me the same result, but it seems that kernel function does not do any thing and it gives me the initial value of u and v. I know due to lack of my experience, the bug may be obvious, but I cannot figure it out. Also, please do not recommend using flatten array, because it is harder for me to understand the indexing in code.
First version:
#include <fstream>
#include <iostream>
#include <math.h>
#include <vector>
#include <chrono>
#include <omp.h>
using namespace std;
const int M = 1024;
const int N = 1024;
const double A = 1;
const double B = 3;
const double Du = 5 * pow(10, -5);
const double Dv = 5 * pow(10, -6);
const int Max_Itr = 1000;
const double h = 1.0 / static_cast<double>(M - 1);
const double delta_t = 0.0025;
const double s1 = (Du * delta_t) / pow(h, 2);
const double s2 = (Dv * delta_t) / pow(h, 2);
int main() {
double** u=new double* [M];
double** v=new double* [M];
for (int i=0; i<M; i++){
u[i]=new double [N];
v[i]=new double [N];
}
for (int j = 0; j < M; j++) {
for (int i = 0; i < N;i++) {
u[i][j]=0.02;
v[i][j]=0.02;
}
}
for (int k = 1; k < Max_Itr; k++) {
for (int i = 1; i < N - 1; i++) {
for (int j = 1; j < M - 1; j++) {
u[i][j] = ((1 - (4 * s1)) * u[i][j]) + (s1 * (u[i + 1][j] + u[i - 1][j] + u[i][j + 1] + u[i][j - 1])) +
(A * delta_t) + (delta_t * pow(u[i][j], 2) * v[i][j]) - (delta_t * (B + 1) * u[i][j]);
v[i][j] = ((1 - (4 * s2)) * v[i][j]) + (s2 * (v[i + 1][j] + v[i - 1][j] + v[i][j + 1] + v[i][j - 1])) + (B * delta_t * u[i][j])
- (delta_t * pow(u[i][j], 2) * v[i][j]);
}
}
}
cout<<"u: "<<u[512][512]<<" v: "<<v[512][512]<<endl;
return 0;
}
Second version:
#include <fstream>
#include <iostream>
#include <math.h>
#include <vector>
using namespace std;
#define M 1024
#define N 1024
__global__ void my_kernel(double** v, double** u){
int i= blockIdx.y * blockDim.y + threadIdx.y;
int j= blockIdx.x * blockDim.x + threadIdx.x;
double A = 1;
double B = 3;
int Max_Itr = 1000;
double delta_t = 0.0025;
double Du = 5 * powf(10, -5);
double Dv = 5 * powf(10, -6);
double h = 1.0 / (M - 1);
double s1 = (Du * delta_t) / powf(h, 2);
double s2 = (Dv * delta_t) / powf(h, 2);
for (int k = 1; k < Max_Itr; k++) {
u[i][j] = ((1 - (4 * s1))
* u[i][j]) + (s1 * (u[i + 1][j] + u[i - 1][j] + u[i][j + 1] + u[i][j - 1])) +
(A * delta_t) + (delta_t * pow(u[i][j], 2) * v[i][j]) - (delta_t * (B + 1) * u[i][j]);
v[i][j] = ((1 - (4 * s2))
* v[i][j]) + (s2 * (v[i + 1][j] + v[i - 1][j] + v[i][j + 1] + v[i][j - 1])) + (B * delta_t * u[i][j])
- (delta_t * pow(u[i][j], 2) * v[i][j]);
__syncthreads();
}
}
int main() {
double** u=new double* [M];
double** v=new double* [M];
for (int i=0; i<M; i++){
u[i]=new double [N];
v[i]=new double [N];
}
dim3 blocks(32,32);
dim3 grids(M/32 +1, N/32 + 1);
for (int j = 0; j < M; j++) {
for (int i = 0; i < N;i++) {
u[i][j]=0.02;
v[i][j]=0.02;
}
}
double **u_d, **v_d;
int d_size = N * M * sizeof(double);
cudaMalloc(&u_d, d_size);
cudaMalloc(&v_d, d_size);
cudaMemcpy(u_d, u, d_size, cudaMemcpyHostToDevice);
cudaMemcpy(v_d, v, d_size, cudaMemcpyHostToDevice);
my_kernel<<<grids, blocks>>> (v_d,u_d);
cudaDeviceSynchronize();
cudaMemcpy(v, v_d, d_size, cudaMemcpyDeviceToHost);
cudaMemcpy(u, u_d, d_size, cudaMemcpyDeviceToHost);
cout<<"u: "<<u[512][512]<<" v: "<<v[512][512]<<endl;
return 0;
}
What I expect from the second version is :
u: 0.2815 v: 1.7581

Answer 1

Your two-dimensional array - in the first version of the program - is implemented using an array of pointers, each of which to a separately-allocated array of double values.
In your second version, you are using the same pointer-to-pointer-to-double type, but - you're not allocating any space for the actual data, just for the array of pointers (and not copying any of the data to the GPU - just the pointers; which are useless to copy anyway, since they're pointers to host-side memory.)
What is most likely happening is that your kernel attempts to access memory at an invalid address, and its execution is aborted.
If you were to properly check for errors, as #njuffa noted, you would know that is what happened.
Now, you could avoid having to make multiple memory allocations if you were to use a single data area instead of separate allocations for each second-dimension 1D array; and that is true both for the first and the second version of your program. That would not quite be array flattening. See an explanation of how to do this (C-language-style) on this page.
Note, however, that double-dereferencing, which you insist on performing in your kernel, is likely slowing it down significantly.