Natural block data decomposition for heterogeneous clusters

We propose general purposes natural heuristics for static block and block-cyclic heterogeneous data decomposition over processes of parallel program mapped into multidimensional grid. This heuristics is an extension of the intuitively clear heterogeneous data distribution for one-dimensional case. It is compared to advanced heuristics for heterogeneous data decomposition proposed for solving linear algebra problems on two-dimensional process grid. We experimentally show that for typical local network (12 Windows 2000 PCs interconnected via Fast Ethernet switch) and for typical linear algebra problems these two heuristics have almost the same efficiency. We demonstrate efficiency of the proposed natural decomposition for case of three-dimensional process grid on the example of 3D modeling of supernova explosion.