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This paper is concerned with the memory usage of sparse direct solvers, which depends on the ordering of the unknowns and the scheduling of the computational tasks. We study the influence of state-of-the-art sparse matrix reordering techniques on the memory usage of a multifrontal solver. Concerning the scheduling, the memory usage depends on the tree traversal and how the tasks are assigned to the processors. We analyze the memory scalability when a dynamic scheduling strategy mainly based on the balance of the workload is used. Finally we give hints to improve the parallel memory behaviour.