Thread: help with parallesation

Hi there,
I need help with the following problem:
Multiplication of a matrix by a vector: Ap = q.
This may sound trivial but in my case it is not simply because the dimensions of A are 250,000,000 by 250,000,000. The variables in my algorithm are:
dataMatrix (50,000,000 x 30) is a matrix that contains indices (addresses) for the matrix A
dataRecord is a row of dataMatrix
nrow is the index to the corresponding Rinv matrix, e.g Rinv(5,9,9)
the call to the function getASddress creates two matrices address containing the indices of A, say A(3,5) and coefficient contains the corresponding values of A(3,5).
p is an input vector and q is the output vector
My attempt to parallelise the algorithm failed with a crash of the program. Any help to fix the problem and improve the speed of the program will be greatly appreciated. Below is the code (note that I am using Blitz arrays):

Code:

                  int numOfThreads = 5;
#pragma omp parallel for num_threads(numOfThreads)
        for (int ir = 0; ir < dataMatrix.rows(); ir++) {
                dataRecord(Range::all()) = dataMatrix(ir, Range::all());
        nrow = getRowForRinv(dataRecord);
// find address and design matrix coefficients
                getAddress();
// calculate contributions            
                t1 = 0.0;
                for (int i = 0; i < newNumFactors; i++) {
        for (int k = 0; k < numberOfTraits; k++) {
        if (address(i,k) == missingValue) {continue;}
                t1(k) += coefficient(i,k)*p(address(i,k));
                }
                }
                t2 = 0.0;
        for (int k = 0; k < numberOfTraits; k++) {
        for (int l = 0; l < numberOfTraits; l++) {
        t2(k) += Rinv(nrow,k,l)*t1(l);
                }
                }
                for (int i = 0; i < newNumFactors; i++) {
                for (int j = 0; j < numberOfTraits; j++) {
        if (address(i,j) == missingValue) {continue;}
                q(address(i,j)) += coefficient(i,j)*t2(j);
                }
                }
                }     // end of data loop