Precalculated Gaussian Kernel added

w-stacking.cu

@@ -22,6 +22,22 @@ gauss_kernel_norm(double norm, double std22, double u_dist, double v_dist)
      return conv_weight;
 }
 
+void makeGaussKernel(double * kernel,
+		     int KernelLen,
+		     double std22)
+{
+
+  double norm = std22/PI;
+  int n = KernelLen, mid = n / 2;
+  for (int i = 0; i != mid + 1; i++) {
+      double term = (double)i / mid;
+      kernel[mid + i] = sqrt(norm) * exp(-(term*term)*std22);
+  }
+
+  for (int i = 0; i != mid; i++) kernel[i] = kernel[n - 1 - i];
+
+}
+
 // Kaiser-Bessel Kernel: it is adapted from WSClean
 double bessel0(double x, double precision) {
   // Calculate I_0 = SUM of m 0 -> inf [ (x/2)^(2m) ]
@@ -188,6 +204,8 @@ void wstack(
     double std = 1.0;
     double std22 = 1.0/(2.0*std*std);
     double norm = std22/PI;
+    double * convkernel = malloc(w_support*sizeof(*convkernel));
+    makeGaussKernel(convkernel,w_support,std22);
     
     // Loop over visibilities.
 // Switch between CUDA and GPU versions
@@ -309,11 +327,14 @@ void wstack(
 
             for (j = jmin; j <= jmax; j++)
             {
+		int jKer = (int)j-pos_u+KernelLen;
+		int kKer = (int)k-pos_v+KernelLen;
                 double u_dist = (double)j+0.5 - pos_u;
 		long iKer = 2 * (j + k*grid_size_x + grid_w*grid_size_x*grid_size_y);
 
 
-		double conv_weight = gauss_kernel_norm(norm,std22,u_dist,v_dist);
+		//double conv_weight = gauss_kernel_norm(norm,std22,u_dist,v_dist);
+		double conv_weight = convkernel[jKer]*convkernel[kKer];
 		// Loops over frequencies and polarizations
 		double add_term_real = 0.0;
 		double add_term_img = 0.0;