C++ for Scientists - Technische Universität Dresden

}
}
// q= A ∗ p;
for (i= 0; i < size; i++)
q[i]= 0;
for (i= 0; i < nnz; i++)
q[aip[i]]+= avp[i] ∗ p[ajp[i]];
alpha= rho / dot(size, p, q);
// x+= alpa ∗ p; r−= alpha ∗ q;
for (i= 0; i < size; i++) {
x[i]+= alpha ∗ p[i];
r[i]−= alpha ∗ q[i];
}
iter++;
}
free(q); free(p); free(r); free(z);
return iter;
void ic 0(int size, double∗ out, double∗ in) { /∗ .. ∗/ }
int main (int argc, char∗ argv[])
{
int nnz, size;
}
// set nnz and size
int ∗aip= (int∗) malloc(nnz ∗ sizeof(double));
int ∗ajp= (int∗) malloc(nnz ∗ sizeof(double));
double ∗avp= (double∗) malloc(nnz ∗ sizeof(double));
double ∗x= (double∗) malloc(size ∗ sizeof(double));
double ∗b= (double∗) malloc(size ∗ sizeof(double));
// set A and b
cg(size, nnz, aip, ajp, avp, x, b, ilu, 1e−9);
return 0 ;
Listing 1.1: Low Abstraction Implementation of CG
As said before the details do not matter here but only the principal approach. The good thing
about this code is that it is self-contained. But this is about the only advantage. The problem
with this implemenation is its low abstraction level. This creates three major disadvantages:
• Bad readability;
• No flexibility; and
• High error-proneness.
The bad readability manifests in the fact that almost every operation is implemented in one
or multiple loops. For instance, would we have found the matrix vector multiplication q = Ap
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