Parallelization, optimization, and performance analysis of portfolio choice models

In this work 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 we use computes the optimal consumption and portfolio choice policy rules over the life-cycle of the individual. Our goal is two-fold. (i) To understand the behavior of a class of emerging applications and provide an efficient parallel implementation and (ii) to introduce a new benchmark for parallel computer architectures from an emerging and important class of applications. We start from an existing sequential algorithm for solving a portfolio choice model. We present a number of optimizations that result in highly optimized sequential code. We then present a parallel version of the application. We find that: (i) 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. (ii) The main challenges in dealing with applications in this area are computational imbalances introduced by algorithmic dependencies and the parallelization method and granularity (iii) We present preliminary results for a problem that has not been, to the best of our knowledge, solved in the financial economics literature to date.