2022-08-14T19:27:52Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/17662016-02-16T01:59:13Zcom_10261_58com_10261_7col_10261_689
Creel, Michael
2007-10-31T12:23:23Z
2007-10-31T12:23:23Z
2005-01-10
http://hdl.handle.net/10261/1766
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.
eng
openAccess
Parallel computing
Kernel regression
Monte Carlo
Bootstrapping
Maximum likelihood
GMM
User-Friendly Parallel Computations with Econometric Examples
documento de trabajo