load to gpu seems to work but the values are different than from python?

src/main.cu

@@ -5,11 +5,43 @@
 #include <iostream>
 #include <cmath>
 
+// Not parallel computation
+__global__ void computeMean(float *ans, size_t *masked_vals, size_t n, float *x) {
+    float sum = 0;
+    size_t num_not_masked_values = 0;
+    size_t num_masked_values = 0;
+    for (int i = 0; i < n; i++) {
+        if (x[i] < 1E14) { /* If x is not missing value */
+            num_not_masked_values++;
+            sum += x[i];
+        } else {
+            num_masked_values++;
+        }
+    }
+    *ans = sum/num_not_masked_values;
+    *masked_vals = num_masked_values;
+}
+
 int main() {
     std::string path = "data/MERRA2_400.inst6_3d_ana_Np.20120101.nc4";
-    std::string variable = "U";
+    std::string variable = "T";
     auto arr = loadDataToDevice(path, variable);
-    cudaFreeArray(arr);
+
+    float *ptr_mean;
+    cudaMallocManaged(&ptr_mean, sizeof(float));
+
+    size_t *ptr_masked;
+    cudaMallocManaged(&ptr_masked, sizeof(size_t));
+
+    computeMean<<<1, 1>>>(ptr_mean, ptr_masked, arr.second, arr.first);
+
+    cudaDeviceSynchronize();
+
+    std::cout << "Mean = " << *ptr_mean << " calculated from " << arr.second << " values where " << *ptr_masked << " are masked values.\n";
+
+    cudaFree(arr.first);
+    cudaFree(ptr_mean);
+    cudaFree(ptr_masked);
 
     return 0;
 }