Communication Avoiding Gaussian elimination

We present CALU, a Communication Avoiding algorithm for the LU factorization of dense matrices distributed in a two-dimensional cyclic layout. The algorithm is based on a new pivoting strategy, which is stable in practice. The new algorithm is optimal (up to polylogarithmic factors) in the amount of communication it performs. Our experiments show that CALU leads to a reduction in the parallel time, in particular when the latency time is an important factor of the overall time. The factorization of a block-column, a subroutine of CALU, outperforms the corresponding routine PDGETF2 from ScaLAPACK up to a factor of 4.37 on an IBM POWER 5 system and up to a factor of 5.58 on a Cray XT4 system. On square matrices of order 104, CALU outperforms the corresponding routine PDGETRF from ScaLAPACK by a factor of 1.24 on IBM POWER 5 and by a factor of 1.31 on Cray XT4.