Distributed cluster-based solution techniques for dense linear equations

In many applications, very large matrixes need be solved, however single or multiprocessor system have some limitations on computing resources, this problem was not solved better. This paper discuss a distributed cluster-based solution for dense linear equations, our works included the definitions of notations, Partition of matrix, communication mechanism, improving of the Guassian Elimination and a master-slaver algorithm etc., the computing cost is O(n(3)/N) the memory cost is O(n(2)/N), the I/O cost is O(n(2)/N), and the communication cost is O(N*n), here, n is the grade of. matrix, N is the number of computing nodes or processes. We show that our distributed cluster-based solution techniques for dense linear equations could solve the double type of Matrix under 10(6) * 10(6) effectively.