I've been trying to design an algorithm for computing determinant of Big square Matrices (N <= 1000). I am allowed to use multithreading, but still, I can't design the algorithm. The assignment states, that I should use Gaussian elimination method. Yet, there are no input data, so the testing data is on me. For big matrices (N ~1000) it's a binary matrix, otherwise - matrix of integers. I wonder, if there exists a relatively time efficient ( less than 10 seconds ) multithread algorithm for this task. Would be glad to hear opinions.
EDIT:: I came up with idea of using partial pivoting to find a row with maximum at current iterating position, swapping it with current row (such heuristic keeps precision the best). And then, the parallel part, is to do row adding in multithread ( I used 8 threads, since it gave the best results on quad-core) if the matrix is big enough. Such method gave more precise results, and efficiency raised with matrix size relatively to one thread method. It actually turned out to be quite easy to implement.