Parallel computation of a quadratic term in Rcpp - r

Question

Let Y and K be an n-dimensional (column) vector and n by n matrix, respectively. Think of Y and K as a sample vector and its covariance matrix.
Corresponding to each entry of Y (say Yi) there is a row vector (of size 2) Si encoding the location of the sample in a two dimensional space. Construct the n by 2 matrix S by concatenating all the Si vectors. The ij-th entry of K is of the form
Kij= f( |si-sj|, b )
in which |.| denotes the usual Euclidean norm, f is the covariance function and b represents the covariance parameters. For instance for powered exponential covariance we have f(x) = exp( (-|x|/r)q ) and b = (r,q).
The goal is to compute the following quantity in Rcpp, using a parallel fashion. (YT stands for Y transpose and ||.||2 denotes the sum of square entries of K).
YTKY ⁄ ||K||2
Here is the piece of code I've written to do the job. While running, Rstudio runs out of memory after a few seconds and the following massage displays: "R encountered a fatal error. The session was terminated". I've very recently started using open MP in Rcpp and I have no idea why this happens! Can anybody tell me what have I done wrong here?
#include <Rcpp.h>
#include<math.h>
#include<omp.h>
// [[Rcpp::plugins(openmp)]]
using namespace Rcpp;
// [[Rcpp::export]]
double InnerProd(NumericVector x, NumericVector y) {
int n = x.size();
double total = 0;
for(int i = 0; i < n; ++i) {
total += x[i]*y[i];
}
return total;
}
// [[Rcpp::export]]
double CorFunc(double r, double range_param, double beta) {
double q,x;
x = r/range_param;
q = exp( -pow(x,beta) );
return(q);
}
// [[Rcpp::export]]
double VarianceComp( double range, NumericVector Y, NumericMatrix s, double
beta, int t ){
int n,i,j;
double Numer = 0, Denom = 0, dist, CorVal, ObjVal;
NumericVector DistVec;
n = Y.size();
omp_set_num_threads(t);
# pragma omp parallel for private(DistVec,CorVal,dist,j) \
reduction(+:Numer,Denom)
for( i = 0; i < n; ++i) {
for( j = 0; j < n; ++j){
DistVec = ( s(i,_)-s(j,_) );
dist = sqrt( InnerProd(DistVec,DistVec) );
CorVal = CorFunc(dist,range,beta);
Numer += Y[i]*Y[j]*CorVal/n;
Denom += pow( CorVal, 2 )/n;
}
}
ObjVal = Numer/Denom;
return( ObjVal );
}