Implementing linear algebra kernels on distributed memory parallel computers raises the problem of data distribution of matrices and vectors among the processors. Block-cyclic distribution seems to suit well for most algorithms. But one has to choose a good compromise for the size of the blocks (to achieve a good computation and communication efficiency and a good load balancing). This choice heavily depends on each operation, so it is essential to be able to go from one distribution to another very quickly. We present here the algorithms we implemented in the SCALAPACK library. A complexity study is made that proves the efficiency of our solution. Timing results on the Intel Paragon and the Cray T3D corroborates the results. We show the gain that can be obtained using the good data distribution with 3 numerical kernels and our redistribution routines.