Modified Gram-Schmidt, Householder transformations and Givens plane rotations are popular methods in linear algebra based on orthogonalization. The major advantage of the orthogonal factorization is its stability. We present a general heuristic strategy of fill-in control in the rank-revealing sparse QR decomposition, which is not very costly for the execution times, using these three algorithms. This strategy is based on column pivoting and it maintains accuracy in the results, as we show experimentally on the AP1000 for a set of sparse matrices from the Harwell-Boeing collection. 1 Introduction QR orthogonalization appears in several applications of linear algebra: linear systems of equations, least squares problems [11], eigenvalue calculation [6, 8], which are necessary to solve in many areas, such as fluid dynamics, circuit simulation, structural analysis \Delta \Delta \Delta Therefore, it is necessary high-quality software for those scientific numeric computations; for instance,...