Multipartitioning is a skewed-cyclic block distribution that yields better parallel efficiency and scalability for line-sweep computations than traditional block partitionings. This paper describes extensions to the Rice dHPF compiler for High Performance Fortran that enable it to support multipartitioned data distributions and optimizations that enable dHPF to generate efficient multipartitioned code. We describe experiments applying these techniques to parallelize serial versions of the NAS SP and BT application benchmarks and show that the performance of the code generated by dHPF is approaching that of hand-coded parallelizations based on multipartitioning.