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User-Friendly Parallel Computations with Econometric Examples

Michael Creel

No 445, Computing in Economics and Finance 2005 from Society for Computational Economics

Abstract: This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave

Keywords: parallel computing; maximum likelihood; GMM; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C13 C14 C15 C63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp
Date: 2005-11-11
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