Simulation of data distribution strategies for LU factorization on heterogeneous machines

This paper analyzes two static data distributions for inherently sequential algorithms such as the LU factorization, namely the group block distribution and the dynamic programming distribution. The aim is to extend previous comparisons to higher problem and machine dimensions and to obtain guidelines for the right choice for a given problem, that is prior to execution the system should be able to choose the distribution that, for the particular problem at hand, achieves the best performance. Note that, contrary to other algorithms, inherently sequential algorithms present additional difficulties with respect to other classes of algorithms, when the goal is to optimize the processing time, due to the fact that the computational load for data matrix columns increases with their index, requiring a fine tuned load assignment and distribution.