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In this paper, we present an analytical performance model of the parallel left–right looking out-of-core LU factorization algorithm for cluster-like architectures. We show the accuracy of the performance prediction model for the ScaLAPACK library. We analyze the overhead introduced by the out-of-core part of the algorithm and we outline a limitation which was never seen before: for large problems the algorithm has a poor efficiency. This overhead is divided into an IO part and a communication part. We derive an overlapping scheme and minimum memory requirement to avoid the IO overhead. The new scheme is validated by a prototype implementation in ScaLAPACK. We show the impact of the communication overhead on two-dimensional distributions. Then we show that with similar memory requirements a second overlapping scheme may be implemented to avoid the communication overhead. If the size of the …