High performance sparse direct solvers are often a method of choice in various simulation problems. However, they require a large amount of memory compared to iterative methods. In this context, out-of-core solvers must be employed, where disks are used when the storage requirements are too large with respect to the physical memory available. In this paper, we study how to minimize the I/O requirements in the multifrontal method, a particular direct method to solve large-scale problems efficiently. From a theoretical point of view, we show that minimizing the storage requirement can lead to a huge volume of I/O compared to directly minimizing the I/O volume. Then experiments on large real-life problems also show that the volume of I/O obtained when minimizing the storage requirement can be significantly reduced by applying algorithms designed to reduce the I/O volume. We finally propose efficient memory management algorithms that can be applied to all the variants proposed.