High Performance Implementation of an Econometrics and Financial Application on GPUs

Abstract

In this paper, we describe a GPU based implementation for an estimator based on an indirect likelihood inference method. This method relies on simulations from a model and on nonparametric density or regression function computations. The estimation application arises in various domains such as econometrics and finance, when the model is fully specified, but too complex for estimation by maximum likelihood. We implemented the estimator on a machine with two 2.67GHz Intel Xeon X5650 processors and four NVIDIA M2090 GPU devices. We optimized the GPU code by efficient use of shared memory and registers available on the GPU devices. We compared the optimized GPU code performance with a C based sequential version of the code that was executed on the host machine. We observed a speed up factor of up to 242 with four GPU devices.