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. Copyright Springer Science + Business Media, Inc. 2005
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Volume (Year): 26 (2005) Issue (Month): 2 (October) Pages: 107-128 Download reference. The following formats are available: HTML
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Nagurney, Anna, 1996.
"Parallel computation,"
Handbook of Computational Economics,
in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 7, pages 335-404
Elsevier.
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Michael Creel, 2005.
"ParallelKnoppix,"
Grecs Computer Code
003.05, Research Group in Computation and Simulations (GRECS).
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Gallant, A. Ronald & Tauchen, George, 1996.
"Which Moments to Match?,"
Econometric Theory,
Cambridge University Press, vol. 12(04), pages 657-681, October.
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