Challenging statements appear recently in the literature on advances in computational procedures like: ‘Tapping the super computer under your desk’ and ‘It is trivial to parallelize a value function iteration using Graphical Processing Units’. These statements refer to the fact that massively parallel computing is becoming an easy and revolutionary tool for speeding up computations in a tremendous way. Applications are spreading rapidly in many fields but occur so far in few areas in economics and finance. Important examples of applications are massively parallel sequential Monte Carlo for Bayesian Inference, in particular particle filtering and/or independence sampling like importance sampling. Other topics are solving dynamic equilibrium models and analyzing agent based models.

However, implementation of GPU-parallelized estimation of econometric models is not so trivial due to requirements of partial independence of the parallel operations, and to data level parallelism that differs from the more common parallel computation model in which parallel processing elements perform separate instruction on separate data. Thus, traditional algorithms must be analytically transformed to allow data level parallelism and implemented in custom GPU kernel code in order to achieve computational speed.

The purpose of this workshop is to discuss the experience and possibilities of massive parallel computing in economics and finance.