We present analytic models and simulation techniques that describe the performance of the multifrontal method on distributed memory architectures. We focus on particular strategies for partitioning, clustering, and mapping of task nodes to processors in order to minimize the overall parallel execution time and minimize communication costs. The performance model has bees used to obtain estimates for the speedups of various engineering and scientific problems, on several distributed architectures. The result is that the available parallelism of these problems is strongly dependent on the sparsity structure of the input matrices. >