Résumé : In this report we present several improvements of widely used parallel LU factorization methods on sparse matrices. First we characterize the L, U factors in terms of their corresponding LU elimination forest. This characterization can be used as a compact storage scheme of the matrix as well as of the task dependence graph. To improve the use of BLAS in the numerical factorization, we perform a postorder traversal of the LU eforest thus obtaining larger supernodes. To expose more task parallelism for a sparse matrix, we build a more accurate task dependence graph that includes only the least necessary dependencies. Experiments compared favorably our methods against methods implemented in the S* environment on the SGI's Origin2000 multiprocessor.