How do I most effectively prevent my normally-distributed random variable from being zero?

Question

I'm writing a Monte Carlo algorithm, in which at one point I need to divide by a random variable. More precisely: the random variable is used as a step width for a difference quotient, so I actually first multiply something by the variable and then again divide it out of some locally linear function of this expression. Like

double f(double);

std::tr1::variate_generator<std::tr1::mt19937, std::tr1::normal_distribution<> >
  r( std::tr1::mt19937(time(NULL)),
     std::tr1::normal_distribution<>(0) );

double h = r();
double a = ( f(x+h) - f(x) ) / h;

This works fine most of the time, but fails when h=0. Mathematically, this is not a concern because in any finite (or, indeed, countable) selection of normally-distributed random variables, all of them will be nonzero with probability 1. But in the digital implementation I will encounter an h==0 every ≈2³² function calls (regardless of the mersenne twister having a period longer than the universe, it still outputs ordinary longs!).

It's pretty simple to avoid this trouble, at the moment I'm doing

double h = r();
while (h==0) h=r();

but I don't consider this particularly elegant. Is there any better way?

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The function I'm evaluating is actually not just a simple ℝ->ℝ like f is, but an ℝᵐxℝⁿ -> ℝ in which I calculate the gradient in the ℝᵐ variables while numerically integrating over the ℝⁿ variables. The whole function is superimposed with unpredictable (but "coherent") noise, sometimes with specific (but unknown) outstanding frequencies, that's what gets me into trouble when I try it with fixed values for h.