User-Friendly Parallel Computations with Econometric Examples
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)
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References listed on IDEAS
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- Swann, Christopher A, 2002. "Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI," Computational Economics, Springer;Society for Computational Economics, vol. 19(2), pages 145-178, April.
- Racine, Jeff, 2002. "Parallel distributed kernel estimation," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 293-302, August.
- Michael Creel, 2005. "ParallelKnoppix," Grecs Computer Code 003.05, Research Group in Computation and Simulations (GRECS).
- Jurgen A. Doornik & David F. Hendry & Neil Shephard, "undated". "Computationally-intensive Econometrics using a Distributed Matrix-programming Language," Economics Papers 2001-W22, Economics Group, Nuffield College, University of Oxford.
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Cambridge University Press, vol. 12(04), pages 657-681, October.
- 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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