Value of sum from thrust::reduce not correct - parallel-processing

Question

I have been trying to implement some code requiring to call reduce on thrust::device_ptr, and the results are not consistent with CPU implementation while dealing with large values. I have to deal with large values. So is there a way around:
My code:
#include <cuda_runtime_api.h>
#include <stdio.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <iostream>
#define NZ 412//
#define NX 402//
using namespace std;
using real =double;
void allocate_array_2d(real**& preal, const int dim1, const int dim2) {
// Contiguous allocation of 2D arrays
preal = new real * [dim1];
preal[0] = new real[dim1 * dim2];
for (int i = 1; i < dim1; i++) preal[i] = preal[i - 1] + dim2;
for (int i = 0; i < dim1; i++) {
for (int j = 0; j < dim2; j++) {
preal[i][j] = 0;
}
}
}
#define cudaCheckError(code) \
{ \
if ((code) != cudaSuccess) { \
fprintf(stderr, "Cuda failure %s:%d: '%s' \n", __FILE__, __LINE__, \
cudaGetErrorString(code)); \
} \
}
int main()
{
real** a;
std::cout.precision(30);
allocate_array_2d(a, NZ, NX);//input array
for (int i = 0; i < NZ; i++) {
for (int j = 0; j < NX; j++) {
a[i][j] = 2.14748e+09;
}
}
real* da;
cudaCheckError(cudaMalloc(&da, NZ * NX * sizeof(real)));
cudaCheckError(cudaMemcpy(da,a[0], NZ * NX * sizeof(real),cudaMemcpyHostToDevice));
///************************
//CUDA KERNELS ARE HERE
// REMOVED FOR CLEAR QUESTION
///*************************
real sum1=0;
thrust::device_ptr<real> dev_ptr = thrust::device_pointer_cast(da);
sum1 = thrust::reduce(dev_ptr, dev_ptr+NZ*NX, 0, thrust::plus<real>());
cout<<" \nsum gpu "<< sum1<<"\n";
real sum2=0;
////////CPU PART DOING SAME THING//////
for (int i = 0; i < NZ; i++) {
for (int j = 0; j < NX; j++) {
sum2 += a[i][j];
}
}
cout<<"\nsum cpu "<< sum2<<"\n";
if((sum2-sum1)<0.001)
std::cout << "\nSUCESS "<< "\n";
else
std::cout << "\nFailure & by "<<sum2-sum1<< "\n";
}
The compiler that I am using is nvcc and my graphics card is nvidia 1650 with compute capability 7.5.

Answer 1

According to the documentation, thrust expects the type for summation to be reflected in the init value:
sum1 = thrust::reduce(dev_ptr, dev_ptr+NZ*NX, 0, thrust::plus<real>());
^
The type of that constant you have is an integral type. If you change that to a double-precision constant:
sum1 = thrust::reduce(dev_ptr, dev_ptr+NZ*NX, 0.0, thrust::plus<real>());
you get matching results, between CPU and GPU, according to my testing. (You could alternatively cast your constant to real type: (real)0 and use that, and there are other ways to address this as well, such as dropping the use of the init value and the binary op.)