Parallelization and Performance of Portfolio Choice Models

We show how applications in computational economics can take advantage of modern parallel architectures to reduce the computation time in a wide array of models that have been, to date, computationally intractable. The specific application comes from solving a portfolio choice model over the lifecycle in the presence of undiversifiable labor income risk, borrowing and short sale constraints. We provide an efficient parallel implementation and introduce a new benchmark for parallel computer architectures from an emerging and important class of applications. We conclude that emerging applications in this area of computational economics exhibit adequate parallelism to achieve, after a number of optimization steps, almost linear speedup for system sizes up to 64 processors on today's hardware shared memory multiprocessors.